Method and apparatus for adaptive transmission techniques with connection context aware radio communication management and path prediction

ABSTRACT

An information handling system operating as a smart vehicle gateway and includes a wireless adapter for communicating with a wireless link and a storage device for storing a spatial-temporal user profile comprising wireless device usage trend data for a location in or near a predicted smart vehicle gateway path during a future time interval for a smart vehicle gateway. The smart vehicle gateway may operate to establish a wireless link on one of several WWAN link options as a home network via a programmable eSIM. The information handling system further includes positional detector and an application processor that determines a trajectory estimation during a future time interval.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a related to U.S. patent application Ser. No. 14/099,686, entitled “Method and Apparatus for Connection Context Aware Radio Communication Management with Predictive Mobile Path,” filed on Dec. 6, 2013, now issued U.S. Pat. No. 9,119,039, issued Aug. 25, 2015, which is a continuation-in-part of U.S. patent application Ser. No. 13/604,906, entitled “Method and Apparatus for Connection Context Aware Radio Communication Management,” filed on Sep. 6, 2012, now issued U.S. Pat. No. 9,088,859, issued Jul. 21, 2015, the disclosures of which are hereby expressly incorporated by reference in their entirety.

Related subject matter is contained in the following co-pending applications:

U.S. application Ser. No. 14/532,343, filed Nov. 4, 2014, entitled “Method and Apparatus for Unified Communication System Involving Context Aware Radio Communication Management for Multiple User Devices,” invented by Will A. Egner et al., and assigned to the assignee hereof.

U.S. application Ser. No. 14/532,455, filed Nov. 4, 2014, entitled “Collaborative Method and System to Improve Carrier Network Policies with Context Aware Radio Communication Management,” invented by Will A. Egner et al., and assigned to the assignee hereof.

U.S. application Ser. No. 14/804,317, filed Jul. 20, 2015, entitled “Method and Apparatus for Connection Context Aware Radio Communication Management,” invented by Will A. Egner et al., and assigned to the assignee hereof.

U.S. application Ser. No. 14/834,091, filed Aug. 24, 2015, entitled “Method and Apparatus for Connection Context Aware Radio Communication Management for a Predicted Mobile Path,” invented by Will A. Egner et al., and assigned to the assignee hereof.

U.S. application Ser. No. 14/886,603, filed Oct. 19, 2015, entitled “Method and Apparatus for Determining Optimized Wireless Link Selection for a Mobile Device Along a Predicted Path,” invented by Will A. Egner et al., and assigned to the assignee hereof.

U.S. application Ser. No. 14/960,596, filed Dec. 7, 2015, entitled “Method and Apparatus for Predicting Mobile Device Wireless Link Quality of Service Requirements Along a Predicted Path,” invented by Will A. Egner et al., and assigned to the assignee hereof.

U.S. application Ser. No. 14/820,387, filed Aug. 6, 2015, entitled “Method and Apparatus for Optimizing End to End Radio Communication Management for Users with Multiple Devices,” invented by Will A. Egner et al., and assigned to the assignee hereof.

U.S. application Ser. No. 14/541,562, filed Nov. 14, 2014, entitled “Method and System for Optimizing Shared Spectrum Utilizing Context Aware Radio Communication Management,” invented by Will A. Egner et al., and assigned to the assignee hereof.

U.S. application Ser. No. 14/996,182, filed Jan. 14, 2016, entitled “Method and Apparatus for a Smart Vehicle Gateway with Connection Context Aware Radio Communication Management and Multi-Radio Technology,” invented by Will A. Egner et al., and assigned to the assignee hereof.

U.S. application Ser. No. 15/002,242, filed Jan. 20, 2016, entitled “Method and Apparatus for a Smart Vehicle Gateway with Connection Context Aware Radio Communication Management and Multi-Radio Technology,” invented by Will A. Egner et al., and assigned to the assignee hereof.

U.S. application Ser. No. 15/010,945, filed Jan. 29, 2016, entitled “Method and Apparatus for a Smart Personal Connect Gateway with Connection Context Aware Radio Communication Management and Multi-Radio Technology,” invented by Will A. Egner et al., and assigned to the assignee hereof.

U.S. application Ser. No. 15/044,033, filed Feb. 15, 2016, entitled “Method and Apparatus for Smart Vehicle Gateway Multi-Hop Networked Communication Using Context Aware Radio Communication Management,” invented by Will A. Egner et al., and assigned to the assignee hereof.

U.S. application Ser. No. 15/061,262, filed Mar. 4, 2016, entitled “Method and Apparatus for a Smart Personal Connect Gateway Multi-Hop Networked Communication Using Context Aware Radio Communication Management,” invented by Will A. Egner et al., and assigned to the assignee hereof.

FIELD OF THE DISCLOSURE

The present disclosure generally relates to a method and apparatus for a radio resources communication management system to adapt to context and usage of communication channels with a smart vehicle gateway.

BACKGROUND

As the value and use of information continues to increase, individuals and businesses seek additional ways to process and store information. One option is an information handling system. An information handling system generally processes, compiles, stores, or communicates information or data for business, personal, or other purposes. Technology and information handling needs and requirements can vary between different applications. Thus information handling systems can also vary regarding what information is handled, how the information is handled, how much information is processed, stored, or communicated, and how quickly and efficiently the information can be processed, stored, or communicated. The variations in information handling systems allow information handling systems to be general or configured for a specific user or specific use such as financial transaction processing, airline reservations, enterprise data storage, internet of things (IOT) monitoring and communications, or global communications. In addition, information handling systems can include a variety of hardware and software resources that can be configured to process, store, and communicate information and can include one or more computer systems, graphics interface systems, data storage systems, and networking systems. Information handling systems can also implement various virtualized architectures. Data communications among information handling systems may be via networks that are wired, wireless, optical or some combination.

BRIEF DESCRIPTION OF THE DRAWINGS

It will be appreciated that for simplicity and clarity of illustration, elements illustrated in the Figures are not necessarily drawn to scale. For example, the dimensions of some elements may be exaggerated relative to other elements. Embodiments incorporating teachings of the present disclosure are shown and described with respect to the drawings herein, in which:

FIG. 1 is a block diagram of a network environment offering several communication protocol options according to an embodiment of the present disclosure;

FIG. 2 is a block diagram of a mobile gateway device according to an embodiment of the present disclosure;

FIG. 3 is a flow diagram illustrating a method of connecting to a wireless network using a context aware radio resource management system according to an embodiment of the present disclosure;

FIG. 4 is a flow diagram illustrating a method of mobile information handling system usage profiling according to an embodiment of the present disclosure;

FIG. 5 is a chart illustrating an example usage profile of a mobile information handling system according to an embodiment of the present disclosure;

FIG. 6 is a flow diagram illustrating a method for wireless link traffic reporting according to an embodiment of the present disclosure;

FIG. 7 is a flow diagram illustrating a method for wireless link energy consumption reporting according to an embodiment of the present disclosure;

FIG. 8 is a flow diagram illustrating a method of establishing optimized wireless link selection for a mobile device along a predicted path according to an embodiment of the present disclosure;

FIG. 9 is a flow diagram illustrating an example method for predicting future mobile device path locations according to an embodiment of the present disclosure;

FIG. 10A is an example embodiment of a bin map for a history of visitation to locations in a user area according to an embodiment of the present disclosure;

FIG. 10B is an example embodiment of a bin map for predicted QoS for a variety of wireless links at locations in a user area according to an embodiment of the present disclosure;

FIG. 10C is an example embodiment of a bin map for locations of a predicted path in a user area according to an embodiment of the present disclosure;

FIG. 11 is a flow diagram illustrating a method for determining mobile device wireless requirements along a path for a mobile device according to an embodiment of the present disclosure;

FIG. 12 is a flow diagram illustrating a method for estimating wireless link QoS levels along a predicted path according to an embodiment of the present disclosure;

FIG. 13 is a flow diagram illustrating a method of operation of a mobile gateway device according to an embodiment of the present disclosure;

FIG. 14A is a flow diagram illustrating a method of operation of adaptive transmission scheduling according to an embodiment of the present disclosure;

FIG. 14B is a flow diagram illustrating a continuation of the method of operation of adaptive transmission scheduling of FIG. 14A according to an embodiment of the present disclosure;

FIG. 15A is another flow diagram illustrating a method of operation of adaptive transmission scheduling according to another embodiment of the present disclosure;

FIG. 15B is a flow diagram illustrating a continuation of the method of operation of adaptive transmission scheduling of FIG. 15A according to an embodiment of the present disclosure; and

FIG. 16 is a block diagram illustrating an information handling system according to an embodiment of the present disclosure.

The use of the same reference symbols in different drawings indicates similar or identical items.

DETAILED DESCRIPTION OF THE DRAWINGS

The following description in combination with the Figures is provided to assist in understanding the teachings disclosed herein. The description is focused on specific implementations and embodiments of the teachings, and is provided to assist in describing the teachings. This focus should not be interpreted as a limitation on the scope or applicability of the teachings.

FIG. 1 illustrates a network 100 that can include one or more information handling systems. For purposes of this disclosure, the information handling system may include any instrumentality or aggregate of instrumentalities operable to compute, classify, process, transmit, receive, retrieve, originate, switch, store, display, manifest, detect, record, reproduce, handle, or utilize any form of information, intelligence, or data for business, scientific, control, entertainment, or other purposes. For example, an information handling system may be a personal computer, a PDA, a mobile information handling system, a consumer electronic device, a network server or storage device, a switch router or other network communication device, or any other suitable device and may vary in size, shape, performance, functionality, and price. The information handling system may include memory, one or more processing resources such as a central processing unit (CPU) or hardware or software control logic, and operates to execute code. Additional components of the information handling system may include one or more storage devices that can store code, one or more communications ports for communicating with external devices as well as various input and output (I/O) devices, such as a keyboard, a mouse, and a video display. The information handling system may also include one or more buses operable to transmit communications between the various hardware components.

In a particular embodiment, network 100 includes networked mobile information handling systems 110, 120, and 130, wireless network access points, and multiple wireless connection link options. Systems 110, 120, and 130 represent a variety of computing resources of network 100 including client mobile information handling systems, data processing servers, network storage devices, local and wide area networks, or other resources as needed or desired. As specifically depicted, systems 110, 120, and 130 may be a laptop computer, tablet computer, or smart phone device that may operate with wireless connectivity. Network 100 may additionally include a smart vehicle gateway 135 associated with vehicle 136. Smart vehicle gateway 135 may be an information handling system with wireless communication capability as well as processing capability. In some example embodiments, smart vehicle gateway 135 may be a vehicle computing system and may interface with processors, memory, and functionality of one or more on-board vehicle computing systems. In other embodiments, smart vehicle gateway 135 may be a separate information handling system which may operate as a wireless network access point. In other embodiments, a mobile gateway device may be a smart personal connect gateway device worn by an individual and having wireless communication capability to interact with network 100.

These user mobile information handling systems 110, 120, and 130, and smart vehicle gateways 135 or another mobile gateway device may access a wireless local area network 140, or they may access a macro-cellular network 150. For example, the wireless local area network 140 may be the wireless local area network (WLAN), a wireless personal area network (WPAN), or a wireless wide area network (WWAN). Since WPAN or Wi-Fi Direct Connection 148 and WWAN networks can functionally operate similar to WLANs, they may be considered as wireless local area networks (WLANs) for purposes herein. Components of a WLAN may be connected by wireline or Ethernet connections to a wider external network. For example, wireless network access points 145 may be connected to a wireless network controller and an Ethernet switch. Wireless communications across wireless local area network 140 may be via standard protocols such as IEEE 802.11 Wi-Fi, IEEE 802.11ad WiGig, IEEE 802.15 WPAN or similar wireless network protocols. Alternatively, other available wireless links within network 100 may include macro-cellular connections 150 via one or more service providers 160 and 170. Service provider macro-cellular connections may include 2G standards such as GSM, 2.5G standards such as GSM EDGE and GPRS, 3G standards such as W-CDMA/UMTS and CDMA 2000, 4G standards such as WiMAX, LTE, and LTE Advanced, and anticipated upcoming 5G standards. It is understood that additional connections may include satellite connection 172 options that may utilize macro-cellular broadband connection protocols described above or may use proprietary satellite connection protocols. Other options may be available as well including macro-cellular options available via base station or other wireless uplink radio connections made available in airborne vehicles such as aircraft, drones, hot air balloons, or similar flying vehicles for certain applications.

Smart vehicle gateway 135 may also interface, via local wireless adapter, with user mobile information handling systems similar to those depicted at 110, 120, and 130 and which are located within vehicle 136. Smart vehicle gateway 135 or other mobile gateway devices may similarly interface, via local wireless adapter, with IoT sensors and devices as well as mobile devices and vehicle computing systems which are located within vehicle 136. Interfacing with devices within vehicle 136 or a local network range may be done using a local vehicle network such as Wi-Fi, Wi-Fi Direct, WPAN, Bluetooth®, Zigbee or another similar local network protocols to wirelessly communicate with mobile devices or sensors within or nearby the vehicle 136 or within range of a local gateway wireless adapter. Located “within a vehicle” or “associated with” a vehicle 136 hereinafter will refer to information handling systems, IoT devices such as sensors, smart vehicle gateways 135, or other computing devices inside of a vehicle, integrated with a vehicle, attached to a vehicle, or nearby a vehicle and which may access the local vehicle network and the smart vehicle gateway local wireless adapter.

The voice and packet core network 180 may contain externally accessible computing resources and connect to a remote data center 186. The voice and packet core network 180 may contain multiple intermediate web servers or other locations with accessible data (not shown). Connection 182 between the wireless network 140 and remote data center 186 may be via Ethernet or another similar connection to the world-wide-web, a WAN, a LAN, another WLAN, or other network structure. Such a connection 182 via WLAN access point/Ethernet switch 145 to the external network is a backhaul connection. The access point 145 may be connected to one or more wireless access points in the WLAN before connecting directly to a mobile information handling system or may connect directly to one or more mobile information handling systems 110, 120, and 130. Alternatively, mobile information handling systems 110, 120, and 130 and smart vehicle gateway 135 may connect to the external network via base station locations at service providers such as 160 and 170 or via satellite service 172. These service provider locations or satellite services may be network connected via backhaul connectivity through the voice and packet core network 180.

Remote data center 186 may include web servers or resources within a cloud environment. For example, remote data centers can include additional information handling systems, data processing servers, network storage devices, local and wide area networks, or other resources as needed or desired. Having such remote capabilities may permit fewer resources to be maintained at the client mobile information handling systems 110, 120, and 130, at smart vehicle gateway 135, or at computing systems or IoT devices within vehicle 136 allowing streamlining and efficiency within those devices. Similarly, remote data center 186 permits fewer resources to be maintained in other parts of network 100.

In an example embodiment, the cloud or remote data center 186 may run hosted applications for systems 110, 120, and 130 and smart vehicle gateway 135. This may occur by establishing a virtual machine application executing software to manage applications hosted at the remote data center 186. Mobile information handling systems 110, 120, and 130 and smart vehicle gateway 135 are adapted to run one or more applications locally, and to have hosted applications run in association with the local applications at remote data center 186. The virtual machine application may serve one or more applications to each of user mobile information handling systems 110, 120, and 130 or smart vehicle gateway 135. Thus, as illustrated, systems 110, 120, and 130 or smart vehicle gateway 135 may be running applications locally while requesting data objects related to those applications from the remote data center 186 via wireless network. For example, an electronic mail client application may run locally at system 110. The electronic mail client application may be associated with a host application that represents an electronic mail server. In another example, a data storage client application such as Microsoft Sharepoint may run on system 120. It may be associated with a host application running at remote data center 186 that represents a Sharepoint data storage server. In a further example, a web browser application may be operating at system 130. The web browser application may request web data from a host application that represents a hosted website and associated applications running at remote data center 186.

In yet another example embodiment, a smart connect manager application may be run at a smart vehicle gateway 135 to determine wireless connection access options based on vehicle location and relevant context and anticipated communication and data needs in accordance with embodiments disclosed herein. Smart connection manager at a smart vehicle gateway 135 may request or send data objects or information to or from an application running at a remote data center 186 or another location such as a context aware radio resource management system remote server 190. In an aspect of the present disclosure, smart connection manager may determine scheduling of data transmissions based on wireless conditions at current and future locations and based on priority levels assigned to various types of data by criticality classification. Context aware radio resource management system remote server 190 may operate a context aware radio resource management system application according to the present disclosure. Similarly, remote access may be available to a remote database with wireless intelligence reports 195.

To communicate within the network 100, the systems 110, 120, and 130 and smart vehicle gateway 135 or any other mobile gateway device will each have a wireless interface module or wireless adapter, hereinafter referred to as a wireless adapter. System 110 includes a wireless adapter, system 120 includes a wireless adapter, system 130 includes a wireless adapter, and smart vehicle gateway 135 includes a wireless adapter in example embodiments. The wireless adapters are operable to provide a wireless radio frequency interfaces, or wireless links, 115 to transmit and receive voice and data between the respective systems 110, 120, 130, and smart vehicle gateway 135 and one or more external networks via wireless network 140 or 150.

Although wireless links 115 are shown connecting wireless adapters to wireless networks 140 or 150, actual wireless communication may link through a wireless access point 145 or a service provider tower such as that shown with service provider A 160 or service provider B 170. A wireless link may also be made between the wireless adapter and another mobile information handling system in a WPAN or Wi-Fi Direct Connection 148. Systems such as 110, 120, and 130 may also have wireless adapters for communicating with a smart vehicle gateway 135 such as when located within a vehicle. Again, WPAN, Wi-FI, Wi-Fi Direct, Bluetooth®, Zigbee or other local wireless communication protocols may be used within the vehicle 136. Further peer-to-peer wireless connectivity between remotely operating mobile gateway devices or mobile information handling systems is contemplated including via similar local wireless communications protocols. With peer-to-peer connectivity, chain or mesh networks may be established for connectivity with network 100. Since one aspect of the disclosed embodiments involves selection of wireless links by a context aware radio resource management system, no particular wireless link selection is depicted in FIG. 1.

The wireless adapters can represent add-in cards, wireless network interface modules that are integrated with a main board of respective systems 110, 120, and 130 or integrated with another wireless network interface capability, or any combination thereof. In an embodiment the wireless adapters may include one or more radio frequency subsystems including transmitters and wireless controllers for connecting via a multitude of wireless links. In an example embodiment, a mobile information handling system or smart vehicle gateway 135 may have a transmitter for Wi-Fi or WiGig connectivity and one or more transmitters for macro-cellular communication. The radio frequency subsystems include wireless controllers to manage authentication, connectivity, communications, power levels for transmission, buffering, error correction, baseband processing, and other functions of the wireless adapters.

The radio frequency subsystems of the wireless adapters may measure various metrics relating to wireless communication. For example, the wireless controller of a radio frequency subsystem may manage detecting and measuring received signal strength levels, bit error rates, signal to noise ratios and other metrics relating to signal quality and strength. In one embodiment, a wireless controller may manage one or more radio frequency subsystems within a wireless adapter. The wireless controller also manages transmission power levels which directly affect radio frequency subsystem power consumption. To detect and measure power consumption by a radio frequency subsystem, the radio frequency subsystem may implement current and voltage measurements of power that is directed to operate a radio frequency subsystem. The voltage and current provides power measurement in milliwatts. Energy consumed may be calculated from sample measurements by taking average power measured over a duration of transmission. In an alternative embodiment of power measurement, counter registers may be used to estimate power consumed during transmissions. Energy measurement may be a sampled during a count cycle. In this case, a sample energy measurement per count is multiplied into a count for operation of a radio subsystem. In this way, power consumption may be estimated.

The wireless adapters may be capable of connecting via a WLAN 140 or a macro-cellular network (WWAN) 150 and service provider 160 or 170 or satellite 172 in a variety of the wireless standards as described above. Each of the wireless adapters for client mobile information handling systems 110, 120, and 130 and smart vehicle gateway are uniquely identified on network 100 via one or more unique identifiers permitting authentication and access. For example, the wireless device can each be identified by one or more Subscriber Identity Modules (SIM), one or more programmable electronic SIMs, one or more of a media access control (MAC) address, an Internet protocol (IP) address, a world wide name (WWN), or another unique identifier such as a user name and password, as needed or desired. For a smart vehicle gateway 135, it may be advantageous to provide for switching between eSIM identifications to permit selection of optimal wireless links to be on a home network, rather than while “roaming.” A smart connection manager operating on a smart vehicle gateway 135 may switch an eSIM to permit election of a new international mobile subscriber identity (IMSI) for election to communicate on a different service provider network selected from multiple available wireless service carriers. In one embodiment, the wireless adapter may be used to establish a plurality of wireless links in accordance with disclosures herein. In another embodiment, a plurality of eSIMs may be available to provide for establishing a plurality of wireless links on more than one wireless service carrier in accordance with embodiments disclosed herein.

Traditional carrier SIMs have a single fixed IMSI and are limited in terms of alternative wireless service carrier selection based on individual negotiated carrier roaming relationships. Often these roaming connections may be more expensive. Additionally, these roaming connections may be less efficient in that link switching to the desired wireless service carrier from the home carrier may require routing to a carrier link location to enable connection. In an alternative embodiment, the radio frequency subsystems of a wireless adapter may contain individual subscriber identity module (SIM) profiles for each technology service provider and their available protocol. These multiple SIM profiles on the mobile information handling system may be provided by one broker such as an MVNO, or by multiple service providers. In many instances, a full set of SIM profiles available from a pool of IMSIs may be checked out and provided. The system may have an application processor for the wireless adapter capable of switching between SIM profiles at the information handling system. The switching between SIM profiles and accessing the service providers may be conducted by information handling systems 110, 120, 130 or smart vehicle gateway 135. Thus, a wireless link recommendation from a context aware radio resource management system would not need to be transmitted to network broker server system 190. Information handling systems 110, 120, 130 or smart vehicle gateway 135 may select a SIM profile for a recommended service provider and protocol and seek direct access. In the case of a network broker server system, billing and other coordination of SIM profile options may be managed by a broker such as an MVNO. The context aware radio resource management system is described further below.

eSIMs allow additionally flexibility in selecting radio connection beyond single carrier and subscriber IMSI systems since an eSIM may be programmable for multiple IMSIs. eSIMs can be used to overcome international roaming restrictions, for example, by enabling smart vehicle gateways to operate within a region as a local carrier on significantly reduced connection rates. eSIMs also offer convenience of reprogramming to a home (or anchor) carrier SIM without having to remove and replace a physical SIM as with traditional carrier SIMs. It further avoids wear and tear on the system such as breakage of a SIM cradle.

Association of a user and a wireless interface module of an information handling system such as a smart vehicle gateway may be made via communications across a networking control plane. For example, a user information handling system may be associated with a user via communication with a database such as Home Subscriber Server (HSS), Active Directory or similar database. This database may reside in the voice and packet core network 180, at a base station at 160 or 170, or elsewhere in the external network.

The wireless adapters may operate in accordance with any wireless data communication standards. To communicate with wireless local area network 140, standards including IEEE 802.11 WLAN standards, IEEE 802.15 WPAN standards, WWAN such as 3GPP or 3GPP2, or similar wireless standards may be used. The wireless LAN network 140 may provide connectivity via Wi-Fi or WiGig for example. The wireless network 140 may have a wireless mesh architecture in accordance with mesh networks described by the above wireless data communications standards or similar standards. Wireless links 115 may also connect to the external network via a WPAN, WLAN or similar wireless switched Ethernet connection. The wireless data communication standards set forth protocols for communications and routing via access point 145, as well as protocols for a variety of other operations. Other operations may include handoff of client devices moving between nodes, self-organizing of routing operations, or self-healing architectures in case of interruption.

Wireless links 115 may connect to a macro-cellular wireless network 150 via one of the service providers 160 or 170 or satellite provider 172. In the depicted example, service provider A 160 may provide wireless data connectivity via a 3G, 4G, or 5G protocol. Service provider B 170 may offer connectivity via a 2.5G, 3G, 4G, or 5G protocol. Any combination of macro-cellular wireless connectivity is possible for each or both of the service providers. The connection quality of service (QOS) and speed of wireless links 115 may vary widely depending on several factors including the service provider bandwidth, the number of mobile information handling systems and users in a location, and other factors. Quality of service impacts energy consumption and efficiency of a mobile information handling system communicating wirelessly. Thus, selection of a wireless link may depend on assessment of the link radio frequency conditions. Radio frequency conditions for wireless links will evolve over time. Differences in wireless link QOS or efficiency will also vary minute-by-minute, hourly, daily, weekly or monthly or during even longer periods. Thus, assessment may need to be regular. This is particularly true for a smart vehicle gateway 135 where vehicle travel may alter conditions depending on location.

Wireless link conditions will vary depending on the type of service likely to be requested by the mobile information handling system. For example, voice communication may be most efficient on a 2G wireless protocol. Voice communication on 4G may be more costly in terms of time required for authentication and connectivity negotiation or in terms of transmission power requirements. Accordingly, voice communication on 4G may have higher required security levels. Data services relating to messaging and SMTP email may have the lowest power cost on 2.5G protocols due to the simplest access security barriers there. As such data services on 2.5G may have a moderate security level by comparison.

In another aspect, higher level data services requiring greater wireless bandwidth may more efficiently use recently implemented protocols. For example, audio streaming may be optimal for 3G protocols. Video streaming and HTTP web browsing may be best suited to 4G protocols and much less efficient at lower protocols which are not designed to accommodate large data throughput.

As the protocols become more advanced, additional registration and initialization for data becomes costly from a processing and power consumption standpoint. This is balanced against the capabilities of the more advanced protocols to handle data transfers. More complicated communication protocols result in greater processing time and authentication/connection message exchange. More robust processor or controller operation and longer delays for transmitter or receiver circuits consume power. On the other hand, certain protocol advancements are designed to make data transfers quicker and more efficient. Thus for example, the 4G protocol may generally consume more power during operation than 2.5G for voice communications, but less power for high volume data transfers.

For this reason, the mobile information handling system operating context can play an important role in determining wireless link conditions and efficiency from a power consumption standpoint. Information about wireless link connection quality and capacity for a service to be used can be advantageous in optimizing communication channel selection. In most cases, transmission or reception via a macro-cellular network 150 base station at a service provider 160 or 170 will take more power than communication via WLAN such as Wi-Fi. Among macro-cellular systems, energy consumption generally, but not in all circumstances, increases at each advancement of technology protocol from 2G to 4G. Plus, increased traffic levels on an advanced macro-cellular protocol may slow down in comparison to an older technology with less active traffic. Additional future macro-cellular protocols are contemplated as well. Those protocols may require additional energy demands of mobile information handling systems.

Factors impacting energy consumption include switching and signaling during communication access, setup, and authentication. Additional factors that impact energy consumption include control communications, latencies, transmission/reception, and switching for the wireless link. As described above, these factors can be specific to the type of wireless service being requested, whether voice, messaging, SMTP, Audio, Video, HTTP or other service types. It can also be specific to the mobile information handling system used. Certain protocols may not be available on some mobile information handling systems. In each instance, radio frequency transmission subsystems and controllers operate and consume device power. Based on these numerous factors, the system of the present embodiment may automatically switch between radio network technologies or service providers to optimize radio frequency conditions, traffic conditions, device power consumption, cost, or any of the above. Selection of a wireless service provider and technology protocol may generally depend on the optimal wireless technology used for a service requested, the radio frequency conditions of a link, traffic conditions for the wireless link, and availability of a link. Wireless service provider may also be referred to as wireless service carrier herein. Technology protocol is also referred to as wireless protocol in some instances herein as well.

Information handling systems 110, 120, 130, and smart vehicle gateway 135 may connect to the network 140 or 150 via an initial default wireless link with one of the service providers 160, 170, satellite 172, or via a WPAN, Wi-Fi, or WiGig connection.

The default wireless link allows the mobile information handling systems 110, 120, and 130 to communicate with the network and in particular with a context aware radio resource management system remote server 190 in one embodiment. The context aware radio resource management system remote server 190 may interface with a network broker system server on the same server location or another server location as described in embodiments herein. The context aware radio resource management system remote server 190 and/or mobile information handling systems 110, 120, and 130 and smart vehicle gateway 135 leverage information from a Wireless Intelligence Report system database 195 and may determine optimal access to a macro-cellular service provider or WLAN.

Optimal, as used herein, refers to those wireless links or service carriers/providers that meet a minimum threshold or set of thresholds for providing wireless service as determined with the context aware radio resource management system. For example, threshold factors such as radio frequency QoS, wireless traffic levels, power consumption requirements, or cost of service, among other factors of a wireless link may determine thresholds which, when assessed in view of expected wireless data and communication usage, are selected as sufficient by the context aware radio resource management system. The context aware radio resource management system may decide that at a remote server 190, or at a local wireless device such as a smart vehicle gateway 135 or other mobile information handling system.

The network broker server system that interfaces with the context aware radio resource management system may be operated as a mobile virtual network operator (MVNO), a wireless service provider wholesaler, a mobile network operator (MNO), or similar type of network broker. For example, in some embodiments, the network broker server system may have contractual bulk access to network services from a variety of mobile network operators or service providers. The contractual bulk access may include pools of IMSIs available for check out to users.

In another example embodiment, the context aware radio resource management system, whether remote or local, may interface with one or more eSIMs to select an IMSI for use with a wireless service provider. The eSIM provides for a wireless adapter to switch between IMSIs and permits a user to elect from among several wireless service providers and protocols as a “home” network. For example, a smart connect manager operating on a smart vehicle gateway 135 may trigger an IMSI switch via one or more eSIMs.

With access to network services from multiple service providers, the context aware radio resource management system may enable access or switch access for information handling systems 110, 120, and 130 among the available service providers. By way of example, a smart connect manager may select an IMSI from among wireless service carriers 160 and 170 as a home network for the smart vehicle gateway 135, and thus for information handling systems or IoT sensors and other devices within vehicle 136.

Information handling systems 110, 120, and 130 or smart vehicle gateway 135 may be multiband capable via the wireless adapters therein. Antenna system frequency and radio protocols for a service provider may be adjusted by way of software programming of transmitter/receiver systems of the wireless adapters in mobile information handling systems 110, 120, 130 and smart vehicle gateway 135. Information handling systems 110, 120, 130 and smart vehicle gateway 135 may be multiband capable via these tunable antennas enabling a wireless adapter to target specific bands depending on the selected service provider and wireless protocol.

The context aware radio resource management system remote server 190 may also access aggregated Wireless Intelligence Report 195 about the performance of service providers 160 or 170 and the various wireless protocols they have made available. The aggregated Wireless Intelligence Reports 195 may be accumulated or crowd sourced from multiple handsets operating on a given network or networks. This feature will be described further below. In one embodiment, Wireless Intelligence Reports 195 may partly comprise mobile wireless traffic reports and may also include spatial-temporal radio-frequency profiles as discussed herein. Mobile wireless traffic reports relate to wireless link conditions including for wireless connections via WWAN, WLAN connections, satellite connections, and other wireless connection options. Wireless Intelligence Reports, or relevant portions thereof, may be transmitted to or stored with mobile information handling systems 110, 120, and 130 or with a smart vehicle gateway 135 in some embodiments. In an example, relevant data for each mobile information handling system 110, 120, and 130, or for a smart vehicle gateway 135 to locations, types of data and communications, or times of operation may be transmitted for local storage. In another aspect, the aggregated Wireless Intelligence Report 195 may be stored on the context aware radio resource management system remote server 190 itself. The selection of a service provider and protocol by the context aware radio resource management system remote server 190 for an information handling system seeking a wireless link will be according to a recommendation received from a context aware radio resource management system agent running on the information handling system. The wireless link recommendation may be a weighted list of service provider options and protocols. It may be submitted by the context aware radio resource management system operating in a smart vehicle gateway 135 or on mobile information handling systems 110, 120, and 130 in some of the described embodiments. Alternatively, the context aware radio resource management system agent could run remotely on the network broker server systems or at a remote data center and use a default wireless link until an optimal wireless link is selected and the smart vehicle gateway 135 or mobile information handling system is switched.

As a mobile information handling system 110, 120, 130 or a mobile gateway device 135 travel, wireless link characteristics will vary based on time and location. For example, wireless conditions for the wireless link will vary with changes in location and time. In an aspect of the present disclosure, the smart connection manager may better utilize wireless link capacity limited during poor wireless conditions by prediction of wireless conditions along a predicted future mobile path. Wireless conditions may be predicted for a location and time from crowd-sourced data and wireless traffic reports provided to the context aware radio resource management system. Predicting a future path for a mobile information handling system 110, 120, 130 or a mobile gateway device 135 travel is described further below in connection to a path prediction system. Additionally, the context aware radio resource management system of the present disclosure may predict wireless link loading based on historical usage trends for a mobile device. With this data, a smart connection manager may adaptively schedule data transmission to avoid overextending limited capacity available on a wireless link during expected poor wireless conditions. The smart connection manager may assign criticality classifications to data before transmission and determine to reschedule transmission of lower priority data. Rescheduling data transmissions may include delaying transmission until a future location is reached with good wireless conditions in one aspect. Rescheduling data transmissions may also include accelerating transmission when future locations are predicted to have poor wireless conditions.

FIG. 2 illustrates a mobile gateway device example. In particular, a smart vehicle gateway 235 is illustrated. A vehicle 236 has an information handling system functioning as a smart vehicle gateway 235. Power for the smart vehicle gateway 235 may be available from various sources including power from the vehicle. Other embodiments may use battery systems to provide power to the smart vehicle gateway. Vehicle 236 may have one or more mobile information handling system within. For example, vehicle 236 may have a first smart phone 231 or a second smart phone 232, or tablet computing system 221 located within the vehicle 236.

Vehicle 236 may also have one or more IoT sensors 255, 265. IoT sensors 255 may be part of the vehicle and may include sensor for engine temperature, speed, fuel consumption, combustion, cabin temperature, chemical sensors and or similar vehicle operational parameters. IoT sensors 265 may involve personal or vehicle payload monitoring sensors. IoT sensors 265 may also include human telemetry sensors for drivers or pilots or may relate to temperature, chemical, lighting, or other sensors relating to vehicle payloads or atmosphere conditions within a vehicle. Other IoT sensors may provide driver road conditions such has average speed for transportation reporting, vehicle tire pressure, and traction along vehicle braking systems. Such IoT sensors may be used for assisting neighboring car travel and for avoidance of traffic accidents in an example embodiment. In other embodiments, IoT radar system sensors can add to enhance an ability of a vehicle to detect and avoid collisions. With the smart vehicle gateway, high wireless connection reliability is improved without cabling and additional weight. In an example embodiment, the variety of IoT devices, such as sensors, allow a company with a fleet of vehicles to better manage transport logistics at a remote command center.

Each mobile information handling system 221, 231, or 232 may have a local wireless link 215 with a smart vehicle gateway 235. Several example protocols are discussed above. In FIG. 3, Wi-Fi is shown as one example connection. Similarly, IoT sensors 255 and 265 may have a local wireless link 215 with smart vehicle gateway 235 as well. The smart vehicle gateway 235 has a local wireless adapter 218 for communication via the local wireless links 215 to devices in the local vehicle network.

The smart vehicle gateway 235 also has a WWAN wireless adapter with an eSIM 275. eSIM of WWAN wireless adapter 275 is a SIM system having software switchable subscriber identity modules for changing an international mobile subscriber identity (IMSI) associated with the smart vehicle gateway 235. By switching international mobile subscriber identity associated with the WWAN wireless adapter 275 of the smart vehicle gateway, the smart vehicle gateway may select to alter its home network upon selection of a wireless service carrier and a wireless protocol. The smart vehicle gateway 235 has a smart connection manager 201 which may be software code or firmware code executed by a processor or controller. Smart connection manager 201 operates in connection with a context aware radio resource management system executable code operating on processors locally or remotely to switch wireless adapters to a selected wireless link from a list of optimal wireless links. For example, smart connection manager 201 may execute instructions to issue commands to an eSIM to switch IMSI's to utilize a wireless service carrier as a home network for purposes of selecting a wireless link from a list of optimal wireless links provided via the context aware radio resource management system.

In one example embodiment, smart vehicle gateway 235 may have a second WWAN wireless adapter with a second eSIM 276. In some embodiments, it is also understood that more than two wireless adapters with separate eSIMs is contemplated. With at least two WWAN wireless adapters each with switchable eSIMs 275 and 276, the smart connection manager 201 of a smart vehicle gateway 235 may establish two or more wireless links to a WWAN for efficient and seamless communication to the WWAN depending on the wireless conditions of a location of vehicle 236. The two or more wireless links may thus be established across the same wireless service carrier or across different wireless service carriers. Each wireless carrier may be utilized as a home network. Further, plural wireless protocols may be established for the plurality of wireless links to improve options for vehicle communication with the WWAN. This may be beneficial for vehicle travel across borders or between ranges of service provider network systems to avoid roaming which may cause increased costs and potential delays. In this way, the smart connection manager 201 of the smart vehicle gateway 235 may leverage the context aware radio resource management system with mobile wireless traffic reports and wireless usage trend data to opportunistically select from established wireless links to a WWAN.

The available open wireless links will have been established from among the optimal wireless links by the context aware radio resource management system. In an aspect, this will have been done with additional factors relating to wireless device usage trend data for the smart vehicle gateway 235 taken into account. In another aspect of the present disclosure, selection of a wireless link to a WWAN for the smart vehicle gateway 235 may be established from among the optimal wireless links by the context aware radio resource management system further taking into account information handling systems 221, 231, and 232 or IoT sensors 255, 265 determined to be within vehicle 236. Wireless device usage trend data for information handling systems 221, 231, and 232 or IoT sensors 255, 265 may impact the selection of the WWAN wireless links by the smart connection manager 201 for example.

In some embodiments, the smart vehicle gateway 235 will also have a satellite radio adapter for satellite based communications. For example, the satellite based communication may be used in some embodiments for connection to the context aware radio resource management system remote server 190. A high priority satellite link may be used to obtain wireless intelligence reports including mobile wireless traffic reports and even wireless device usage trend data stored remotely. In other examples, a context aware radio resource management system remote server 190 may perform the analysis of a mobile information handling system or the smart vehicle gateway 235 to provide a list of optimal wireless links via satellite for use by a smart connection manager 201. In yet other embodiments, voice, data, and other communications via satellite may be considered as an option from among the upstream wireless link options available to the smart vehicle gateway 235.

In some embodiments, when a smart vehicle gateway is set to establish a wireless link to a WWAN from a smart vehicle gateway, the IMSI options may be provided from a network broker system. In an example embodiment, the network broker system may provide IMSIs to fleets of vehicles. The network broker system has pools of IMSIs that may be checked out for a wireless adapter based on location. The pools of IMSIs at the network broker system include multiple wireless service carriers and several IMSIs from each wireless service carrier are licensed to the network broker system. For example, an IMSI pool may include four wireless service provider IMSI options in one embodiment. Previously, network broker systems would send all available IMSIs options, one for each wireless service provider available, via over-the-air (OTA) activation to a user's wireless adapter. Thus, no analysis was conducted to determine which wireless service carriers were to be used. It is beneficial for a wireless adapter such as a smart vehicle gateway to limit the IMSI options from a network broker system and limit the IMSIs checked out to it. A context aware radio resource management system may be used to filter the IMSI wireless service provider options checked out from the pool of IMSIs at the network broker system.

With a smart filtering or screening of the IMSIs requested from a network broker system, the network broker system may be able to carry a fewer number of IMSIs to supply the pools for checkout by users. This may in turn reduce costs at the network broker by requiring fewer licenses to the IMSIs. That cost savings may also reduce costs for a user.

In addition, the context aware radio resource management system will request only IMSIs for optimal wireless link options as determined by link ratings described in the present disclosure. Thus, when a smart vehicle gateway or mobile information handling system switches to an optimal WWAN wireless link, an IMSI corresponding to the WWAN wireless link provides for that communication to be on a “home” network. This avoids roaming connections. It is understood that roaming connections may be more expensive to operate on a wireless service provider. Additionally, roaming connections may be substantially less efficient. In some cases, a roaming connection from a home network of an IMSI must be routed back to a home network link with the desired alternative wireless service provider. This can require additional communication links to achieve. A direct connection via an access to a home wireless network may be more efficient and less costly. Thus, selecting or switching between optimal wireless service providers by switching IMSIs may yield cost savings on a cost per gigabyte basis.

It is understood that cost per gigabyte may also vary between wireless links available from the list of optimal wireless links determined via a context aware radio resource management system as described herein. Cost per gigabyte on wireless service carriers may vary among WWAN links. For example, a vehicle travelling across borders may be subject to substantial cost fluctuations among WWAN carriers. In another example embodiment, some wireless links, such as non-WWAN links may be less expensive as well. Settings for a smart vehicle gateway or a mobile information handling system may serve to prioritize cost per gigabyte based on location when selection from optimal wireless links is made. Moreover, a smart connection manager may select between a plurality of simultaneous wireless links established for a smart vehicle gateway. The basis of selection may be on quality of the links available, traffic levels, or suitability to expected data needs. However, the basis of selection may also be based on a cost per gigabyte basis to select the most cost efficient option when available.

FIG. 3 illustrates a context aware radio resource management method for use in selecting a network and technology within wireless network 100 at a given location. Several factors are assessed by the context aware radio resource management method in selecting a radio technology and a service provider. A software agent is deployed at a mobile information handling system or elsewhere in the network for executing the context aware radio resource management method. In one example embodiment, the context aware radio resource manager may reside at a mobile information handling system or smart vehicle gateway such as 135 and may interface with the cloud based context aware resource management system server such as 190. At step 310, the context aware radio resource management system software agent obtains user profile data. The user profile data establishes an approximate cyclostationary usage pattern of the mobile information handling system. The time of day, location, types of usage, and usage percentages during a sample time interval are example factors included in the user profile data. This user profile data also may include a confidence of the estimate. This may be a statistical measurement of a mean and standard deviation for a set of data. Alternatively, the confidence of estimate may involve a goodness of fit metric to an expected set of values. Alternative statistical analysis may be performed on the user profile data to provide a confidence of the estimate. In the context of a smart vehicle gateway, a smart connection manager operating some or all of a context aware radio resource management system may obtain user profile data for the smart vehicle gateway 311. In some embodiments, the smart connection manager of the context aware radio resource management system may obtain user profile data for mobile computing devices within the vehicle 312 and for IoT devices and sensors 313.

At step 320, the context aware radio resource management system receives wireless link radio frequency wireless traffic reports. For location and time, available radio technologies and service providers are listed. The reports contain data relating to location, time and a radio frequency profile of given radio technologies for the available service providers. The data may also include an associated confidence of estimate. The wireless link radio frequency profile may combine recent reports, historical traffic reports, as well as data measured via an active device radio frequency scan. In some embodiments, in order to minimize mobile information handling system battery power consumed, radio frequency wireless traffic reports from the network may only be requested or sent when a service provider network or a mobile information handling system detects a significant change in signal quality or the network broker server detects that the local crowd source information is out of date.

The context aware radio resource management system receives battery power level data at step 330 from an intelligent battery management system of the mobile information handling system. The battery power level input may determine that certain wireless communication protocols are too costly in terms of power. Below a defined battery level threshold, the context aware radio resource management system may disable the most advanced protocols to save energy. For example, with only 10% battery power remaining, the context aware system may recommend to a user to disable high power consuming protocols such as 4G. The option may be given to the user, or automatic shut down of the radio frequency subsystem may take place. In a further example, the context aware system may recommend or shut down 3.5G at 5% remaining battery power. Any threshold levels may be set to trigger recommended shut down. This data provides the context aware radio resource management system with an ability to manage the mobile information handling system power consumption when battery levels are low. The context aware radio resource management system may switch wireless protocols being when receiving a shut down recommendation. The switching may happen with a continuous connection to the same service provider.

The intelligent battery power management may also determine which services or protocols are unavailable at a given location. This information may come in part from radio frequency profile data in the radio frequency wireless traffic reports. In that case, the radio frequency subsystem transmitters, receivers, and controllers associated with unavailable protocols may be turned off by the context aware radio resource management system. For example, if no 4G WWAN is detected, the radios capable of communicating with these protocols may be turned off in the mobile information handling system. As before, the option may be recommended to the user of the mobile information handling system before shutting a subsystem down.

Step 340 depicts that a variation of the mobile wireless traffic reports may be used by the context aware radio resource management system. This variation is a link energy consumption report. These energy link reports contain data relating to time, location and radio frequency profile information similar to the radio frequency wireless traffic reports 320. In addition, measurements of energy consumed during use of a specified wireless link for a specified wireless service type is reported in the energy link reports 340. The energy link data profile matrix can provide more detailed information above the mobile wireless traffic reports. As with other input factors, a confidence of estimate associated with this data may be included. The energy link report data may combine recent energy link profiles, historical energy link reports, and measurements through mobile information handling system scans during operation.

At method step 350, the context aware radio resource management system receives the user profile data 310, the wireless link radio frequency wireless traffic reports 320, and battery power level data 330. Alternatively, the energy link reports 340 may be received as a variation of the wireless link radio frequency wireless traffic reports 320. These inputs are assessed by the context aware radio resource management system software, such as in connection with a smart connection manager for the smart vehicle gateway, at 350. The context aware radio resource management system software determines the optimal radio frequency technology protocol and service provider to be used. This determination is based, at least in part, on some subset of data in the input reports. Also, the settings such as what protocols are available, which protocols have been shut down, or what power is required to transmit on a given protocol are determined for the mobile information handling system. Again, optimal refers to those devices which meet several threshold criteria determined by the context aware radio resource management system.

In one embodiment, the wireless link assessment 350 may result in a ranked list of service providers that are optimal due to meeting various requirements. Using user profile reports 310 and radio frequency link reports 320, each service provider may be given an overall rank as follows: Service Provider Rating(j)=Σ_(i=1 to k)(User Profile by Technology*Link Rating), where i=a technology index,j=service provider index, and k=the number of wireless technologies.

The service providers can be ranked by this score. For a matrix of link protocols=[2G, 2.5G, 3G, 3.5G, 4G], an example user profile by technology may result in the following matrix (30%, 25%, 15%, 30%, 0%). The user profile shows the anticipated protocol usage score from a location and time period. A Link Rating (j) may result in the following matrix (70%, 80%, 95%, 90%, 30%). The link rating shows a quality of service score by protocol for a service provider at a location and time. The service provider rating for a user profile in this example would result in 0.8225. Altering the weight of factors may increase or decrease the relevance of certain protocols depending on the change to the calculations. Either the user profile scores or the link ratings may change the calculations of the scores assigned there. This is described further below. The above values serve only as an example.

Battery power levels 330, energy link reports 340, and additional factors, such as subscriber cost of wireless link usage, may also be assessed to select a wireless link. Subscriber cost or settings may influence the determination by weighting protocol options and influence the scoring described below. Alternatively, settings or subscriber cost may be used to mask out protocol options altogether.

The selection of a wireless link by the context aware radio resource management system may depend on the factors and settings described above. For example, if optimal speed of connection is the goal with less consideration of power consumption, the weight assigned by the context aware system to input data may be influenced. This may be the case if the context aware resource management system detects a connection to an AC power source. User profile data 310 showing usage and the wireless link radio frequency wireless traffic reports 320 indicating link quality and capacity will be more heavily weighted. Energy consumption data may be less heavily weighed. If on the other hand, lower power consumption and long battery life are optimal considerations, battery power level data 330 and the energy link reports 340 may be more heavily weighted. Any combination of weighting involving anticipated usage, radio frequency channel quality, battery power levels, or efficient power consumption may be used in the present embodiment.

Upon determination of an optimal link or links, the context aware radio resource management system provides a command to select a preferred wireless link protocol and service provider. In an alternative embodiment, a list is created providing a preferred set of wireless links and protocols. The context aware radio resource management system may also list wireless links in rank order as described above.

Turning to 355, a smart connection manager may determine from the context aware radio resource management system list for a selected wireless service carrier link protocol for an optimal wireless link protocol. With the weighted list, the smart connection manager may determine a preferred service provider and protocols for the location of the smart vehicle gateway. The smart connection manager may then issue a command to the eSIM of the smart vehicle gateway to select an IMSI corresponding with the selected wireless service carrier as a home network. In doing so, the smart vehicle gateway may avoid roaming while selecting an optimal wireless link for communication by the vehicle mobile information handling systems or IoT devices.

At method step 360, a request is made for access to the selected network. The context aware radio resource management system transmits a command to the selected wireless link provider for the desired protocol. The smart vehicle gateway uses the context aware radio resource management system list to command to an application processor controlling eSIM profile selection within the smart vehicle gateway. The command to the eSIM will adjust the IMSI under which the wireless adapter is operating when it is desired to switch wireless service carriers. Then the wireless adapter negotiates access to the preferred service provider and selects a protocol.

At step 370, if the access request is accepted by the service provider, the mobile information handling system is connected to the selected service provider and wireless protocol. If access is declined, the wireless adapter will request access to another preferred protocol at the service provider. It that still does not succeed, then the smart connection manager may command the eSIM to switch to a different IMSI for another wireless service provider in the weighted list received from the context aware radio resource management system. If the list is in rank order, then one embodiment the smart connection manager may turn to each next-ranked protocol and service provider in order on the list. This repeats until a satisfactory optimal wireless link is found and access made for the mobile information handling system.

FIG. 4 illustrates a method 400 for generating an end-user profile in the context aware radio resource management system. In the first step 410, the context aware radio resource management system software agent is started to optimize device performance in selecting a wireless link. Some or all of the context aware radio resource management may operate at a mobile information handling system or smart vehicle gateway to measure and monitor device data and communication usage. At step 420, the context aware radio resource management system software agent initiates a baseline device profile state. The device profile state reflects expected usage for the mobile information handling system. It includes various usage service types. Example usage types may include voice, audio streaming, video streaming, internet usage, email communication, SMS or other messaging. For a smart vehicle gateway or other mobile gateway device, usage types may be impacted by mobile information handling systems and other computing systems within the vehicle or the local network for the gateway device. Moreover, the device profile of a smart vehicle gateway will also be impacted by an IoT sensors or other devices that wirelessly transmit through the smart vehicle gateway to establish a usage profile for the smart vehicle gateway or other mobile gateway device. In some embodiments, individual device profiles may also be established for the individual information handling systems or IoT sensors and devices within the vehicle or within a local mobile gateway network.

A previous user data profile collected for the operation of the mobile information handling system, smart vehicle gateway, or IoT sensors/devices may serve as the baseline device profile for the respective device. Such a profile is specific to the location of the device and to a time slice during which operation is being optimized. Locations may be assigned to geographic zones such as a campus, city, borough, county, etc. Time may be assigned to defined time periods during a day but may differ across days of the week. This zoning and time definition is optional but will help control the number of different user profiles generated.

In one embodiment, a set default user profile may be used as a baseline. For example, the client service profile may assume SMS messaging consumes 10% of device usage, voice communications consume 30%, video streaming consumes 10% of usage, audio streaming consumes 15% of usage, SMTP email consumes 10%, and internet activity consumes 25%. This baseline state may be specific to the mobile information handling system type. For example, the mobile information handling system may be geared toward usage on a certain network protocol. For example, certain systems may be optimized to operate on a 3G or 4G network. Additionally, a default service provider and wireless protocol may generally be assigned to the mobile information handling system. This default wireless link may affect or set the baseline profile state.

At step 430, the context aware radio resource management system may initiate operational measurements according to time of day, location of mobile information handling system, and usage levels for various usage types. The usage data measurements may be taken during sample intervals. For example, during a time period from a specific location zone, the context aware radio resource management system may monitor operation of the mobile information handling system. It will measure the dwell time or use percentage of each type of service. This can include measuring minutes used or number of calls made for voice service. It can measure bytes transferred or number of requests made for video streaming or audio streaming. It may measure the number of messages sent and received or bytes transferred for SMTP, SMS, or similar messaging. The context aware radio resource management system can also measure the data requests and responses or data volumes exchanged in internet accesses. At each sample interval, the available service providers and available wireless link protocols may be determined as well.

The results of the measurements are incorporated into a user profile at step 430. Rather than strictly relying of total data volumes or number of requests, the measurements may be scaled or normalized to reflect a percentage of service usage. This normalized scoring permits comparison. The normalization may be scaled to permit scores of usage reflecting importance or frequency of access to the service types. For example, while audio/video streaming may take a large amount of data, usage may be uncommon. For the same time period, the voice service usage or SMTP messaging may be substantially more frequent but may not transfer as much data volume. Therefore, service recommendations may be better scaled toward frequency of accesses rather than total data throughput volumes. If on the other hand video streaming is a daily occurrence, even if only one request occurs at that time period, then scaling may lean toward total data volume. In this case, normalization scaling in favor of data throughput volume may more accurately reflect the usage.

The use or usage percentage may be measured and scored according to the preferred parameters set in the context aware radio resource management system. It may also be averaged with the baseline default or historical user profile state if so desired. For example, previously measured usage data for a location zone and time period may provide higher data confidence if averaged into measured data.

Measurements may be repeatedly taken in later sample intervals at step 440. Such measurements may be conducted throughout an entire day and over the course of several days or longer. The multiple sample measurements of the mobile information handling system usage comprise a spatial-temporal user profile. The spatial-temporal user profile may have an associated confidence estimate. At step 450, the spatial-temporal user profile and any confidence estimate will be stored either at the mobile information handling system or elsewhere in an available database. The spatial-temporal user profile for the mobile information handling system usage assists in selection of radio frequency links for given times and location zones. The user profile may predict the predominantly used combination of services typical of the mobile information handling system during a time period or from a certain location. The predicted service usage assists in selecting an optimal service provider and radio frequency protocol. The spatial-temporal user profile information will be stored in the mobile information handling system to protect end-user privacy information.

To apply this data to selection of a wireless service provider and protocol, the usage percentage levels are mapped to service protocols available to a mobile information handling system. The mapping of use percentages to a protocol may involve assigning the use percentage for a service to the lowest power consuming protocol available for a usage type. In other words, the service type usage score is mapped to the technology protocol most efficient for that service type. For example, voice communication usage may be assigned to a 2G protocol whereas audio or video streaming may be assigned to 4G. These energy efficiency rules are stored as part of system parameters. These parameters are used to map services to optimal wireless technology. The parameters may also be adjusted as a function of energy state or battery power levels of the mobile information handling system. The parameters may also be specific to the make or model of the mobile information handling system and its capabilities in processing, memory, radio frequency transmission systems and other features. Similarly, operational capabilities or battery or radiofrequency states of the smart vehicle gateway may impact the parameters assessed by the context aware radio resource management system.

Once the usage levels are measured and scaled according to anticipated importance of data throughput versus frequency of access, the result may score messaging at 20% of usage, voice at 30% of usage, video at 10% of usage, audio at 15% of usage, SMTP at 5% of usage, and internet at 20% of usage. For optimizing minimal power consumption, each service usage is mapped to a service protocol. For example, voice may consume the least power on a 2G network. If 2G cannot accommodate video streaming, it may be eliminated however. The voice score is associated with the most efficient choice available. If video streaming is very infrequent at less than 5%, then elimination of 2G protocol may be disregarded. Should the rare video streaming service request occur, the cost of switching protocols may be worthwhile at that time. Switching protocols may even occur within one service provider to minimize cost of access, negotiation, authentication, and switching with a different service provider.

In the present example, messaging and SMTP email are optimal at 2.5G. The email usage score is then mapped to 2.5G. 3G may consume more power, but also may be determined to provide audio streaming services most efficiently. Thus, the audio streaming usage score is mapped to 3G. Internet access and video streaming may be most efficient in a 4G protocol and thus mapped to this protocol. If 4G is unavailable, then 3.5G may be selected instead if it is the next most efficient protocol level.

The mapping will result in a service profile of protocol technology assigned according to optimal power consumption efficiency for the services anticipated for a mobile information handling system. For example, 2G may be weighted with a value of 30% as optimal for voice usage. 2.5 G may be weighted at 25% as optimal for SMS messaging and SMTP email messaging. 3G may be weighted at 15% as optimal for audio streaming usage. And 3.5G may be weighted at 30% for video streaming and http internet access in the case that 4G is unavailable. For a matrix of link protocols=[2G, 2.5G, 3G, 3.5G, 4G], a user profile by technology may result in the following example matrix (30%, 25%, 15%, 30%, 0%). This spatial-temporal user profile data is then utilized by the context aware radio resource management system alone or in combination with other profile reports shown in FIG. 3 to select a wireless link.

FIG. 5 illustrates a graphical example of spatial-temporal user trends for a mobile information handling system. As shown in FIG. 5, user trend behavior measurements are shown as a function of time and location. Five usage types are illustrated in this example, voice, video streaming, audio streaming, email, and internet usage. Usage amounts are shown along the x-axis. The y-axis depicts time and various locations. In this example, three locations and time periods are defined, though more or fewer could exist. Those time periods are early morning usage at Location 1, business hours usage at Location 2, and after work hours at Location 3. Each general time period may be comprised of multiple time slices with separate data samples. The mobile information handling system may apply a curve fitting approach to the user profile data to compress information associated with a level of use per type of service per unit of time. An n-order polynomial approach may be used to reduce information to N parameters.

Email usage is depicted in trace 510. Voice bandwidth usage is depicted in trace 520. Internet usage is depicted in trace 530. Audio streaming usage is depicted in trace 540. And video streaming usage is depicted in trace 550. In many cases, the user profile data can be expected to be cyclostationary. In other words, the usage trends repeat themselves. For example, usage may repeat itself daily during a business week. In the example of FIG. 5, voice bandwidth usage 520 increases mid-day during business hours at location 2 during lunch. Voice bandwidth consumption 520 will again increase during after work hours at location 3. This may include increasing during a commute home or upon returning home. Similarly, trends in email usage 510 may show peaks at all three locations with lower bandwidth usage trends arising during non-break business hours at location 2 and late in the evening after work at location 3. Thus, despite variability in these usage schedules, some cyclostationary consistency can be established. For this reason, time period data may be averaged for weekdays or may be specific to Wednesdays depending on the trends. Variability may be accounted for with confidence estimates on the data.

The cyclostationary trends may apply to mobile information handling systems that operate as mobile gateway devices. For a smart vehicle gateway as described herein for example, a graphical example of spatial-temporal user trends may vary significantly depending upon vehicle usage. A fleet vehicle, such as a semi-truck, may be operational and transmitting IoT sensor and device data during business hours. A long haul vehicle such as a cargo train, cargo jet, or trucking vehicle may operate at extended hours overnight in another example. A commuter vehicle on the other hand may have significant wireless communication activity during commute times, but be otherwise limited as to wireless activity between commute times. The graphical example of spatial-temporal user trends of FIG. 5 is but one example.

FIG. 6 shows a method 600 for establishing a mobile wireless traffic report for a wireless link for wireless links. The mobile wireless traffic report partially comprises a spatial-temporal radio frequency profile for the wireless links. The system begins with a baseline mobile wireless traffic report available from a network broker system or available from cooperative service providers if no previously measured data is available. By way of example, baseline data may be drawn from available wireless coverage maps.

Key performance indicators (KPI) comprise a spatial-temporal radio frequency profile. Data such as received signal strength (RSSI), signal-to-noise ratios (SNR), or signal to interference ratios (SIR) may be relevant channel quality indicators in a KPI matrix. Other data, may include data throughput speeds and communication latencies. One or more of these performance indicators may be used to compute a link rating for a wireless link. Baseline reports rely on estimated values. For example, using baseline estimated received signal strength indicators (RSSI), a link rating may be computed as follows: Link Rating(i,j)=MAX(MIN(100%,(Estimated RSSI Signal Carrier−Minimum Signal)/Max RSSI signal−Minimum RSSI signal,0%), where i is a technology index and j is a service provider index.

A maximum RSSI level may be defined in a technology protocol, for example as −70 dBm. The minimum RSSI level may be defined as well, for example at −110 dBm. RSSI is not the only key performance indicator that may be used to compute link ratings. Link rating may be based on different key performance indicator values besides received signal strength. Alternatively, multiple key performance indicator values may be used in the computation of a link rating.

A link rating matrix is established by link protocols for a service provider. For a matrix of [2G, 2.5G, 3G, 3.5G, 4G], the baseline Link Rating (j) computation may result in (70%, 80%, 95%, 90%, 30%). 100% indicates best signal link quality and 0% indicates a signal quality below a minimum acceptable level. The Link Rating (j) evaluates a service provider overall. The context aware radio resource management system may use the link rating scores to evaluate the optimal wireless service providers and available protocols for the anticipated usages. Once a service provider is selected, the context aware radio resource management system may switch between protocols within one service provider depending on changes in usage. Thus, the link rating protocol matrix can assist in selecting a service provider with the best scores in multiple protocols. In another aspect, the smart connection manager of a smart vehicle gateway may select an IMSI, with an embedded wireless service carrier identification, corresponding to a selected service provider. This selection may be based on Link Rating (j) in one embodiment. In another embodiment, the selection may be based a wireless link protocol having a high enough score for inclusion on an optimal wireless link list. The IMSI selection may then be based on which wireless service carrier provides the ranked wireless protocol. The smart connection manager may then direct the eSIM to switch to an IMSI corresponding to one of the optimal wireless service carriers as a “home” network. Thereby, the smart vehicle gateway or other mobile information handling system may avoid roaming and potentially avoiding a network broker system to connect to wireless links for a wireless service carrier.

At block 610, a context aware radio resource management system operating on a mobile information handling system or in a smart vehicle gateway may scan for wireless link mobile wireless traffic reports fitting a time and location zone for operation. Wireless link mobile wireless traffic reports may be retrieved from a central server database, such as context aware radio resource management system server 190, connected to the wireless networks 140 or 150. Alternatively, they may be located elsewhere in a database such as at a network broker server system. The baseline report may be supplemented or superseded by any fresh or historical mobile traffic reports to assist in selecting a service provider and protocol. Recent or historic radio frequency profiles for time period and location zone may be used to update or supplement the wireless link mobile wireless traffic reports. More recent data may be of greater relevance however. For example, the link ratings in a radio frequency profile may utilize recently measured RSSI values instead of estimated values.

Mobile wireless traffic reports are aggregated via crowd sourcing. They may be categorized by location zone and have time and date stamps to identify freshness. Crowd sourcing of information will enhance the availability of accurate data for location zones and times of mobile information handling system operation. For example, if a mobile information handling system makes a request for a fresh mobile wireless traffic report, the central server database may have reports from other mobile information handling systems with recent timestamps. Alternatively, the central server database may make a request for a recent mobile wireless traffic report from mobile information handling systems in the same location. Whether via recent storage in the central database or via a recent request of fresh crowd sourced mobile wireless traffic reports, such a report may avoid the need for the mobile information handling system to conduct a radio frequency scan itself.

Crowd sourcing mobile wireless traffic reports for locations and times provides a higher chance that a current mobile wireless traffic report for a location is available. It also increases the available data points providing greater certainty and reliability of data. Part of the benefit of crowd sourcing may also involve performing a hysteresis analysis on the data coming from multiple mobile information handling systems to determine trends in wireless link selection. When a wireless link is reported having low traffic and good radio frequency conditions, traffic from systems using the context aware radio resource management system will elect that wireless link. If a large part of the crowd of mobile information handling systems begin to pile onto whichever wireless link is reported to have the best available bandwidth, that link will slow down and underperform. The mobile wireless traffic reports account for this by conducting a hysteresis analysis. If a large number of users begin to select this wireless link, then the method for generating mobile wireless traffic reports accounts for this traffic and alters the recommended wireless links. For example, a second best option may be recommended as optimal for traffic and radio frequency conditions instead. Each crowd sourced mobile wireless traffic report identifies its selected link. A count of these selections can be compared to a threshold rate level of selections for a given link. If the rate of selections exceeds the threshold for a link, then the recommendation may be altered.

At block 620, the method determines whether a fresh mobile wireless traffic report is available for the location of the mobile information handling system or smart vehicle gateway. If so, a fresh mobile wireless traffic report is retrieved from a central server database. At 630, the method assesses the fresh mobile wireless traffic reports and any available historical mobile wireless traffic reports. Historical mobile wireless traffic reports may be stored locally for the mobile information handling system or smart vehicle gateway or received from a central server database. Assessment of both fresh and historical data is used to determine one or more optimal wireless links at step 630. The combination of fresh and historical information provides a radio frequency channel performance assessment of the wireless links. While fresh report data may be weighted more, historical data may add additional depth of data. The context aware radio resource management system elects a wireless link based, at least in part, on the radio frequency channel performance profile as described in FIG. 3.

If no fresh mobile wireless traffic reports are available at step 620, the method seeks stored historical mobile wireless traffic reports from the central server database at step 640. Depending upon the age of these historical mobile wireless traffic reports and the estimated confidence associated with that data, the method will establish a radio frequency channel performance profile based on historical mobile wireless traffic reports at step 650. If there are no reliable historical mobile wireless traffic reports recent enough to base an assessment upon, the context aware radio resource management system initiates a mobile information handling system radio frequency scan. This scan collects data regarding possible wireless links at step 660. This radio frequency scan consumes power and processor resources so should be used sparingly, however it provides up-to-date key performance indicators (KPI) for a new radio frequency profile to be used in a mobile wireless traffic report. Based upon this new mobile wireless traffic report, the system provides a wireless link performance profile to be used by the context aware radio resource management system.

Additionally, in some embodiments of the present disclosure, the smart vehicle gateway may operate a context aware radio resource management system and radio frequency profiles for local wireless links within a vehicle. The local wireless links may then be determined based on performance for connections between mobile information handling systems or IoT devices and sensors within the vehicle and the smart vehicle gateway. These radio frequency profiles may be used by the context aware radio resource management system to determine optimal downstream local wireless links for the mobile information handling systems or IoT devices and sensors within the vehicle. Based upon this data, the smart connection manager of the smart vehicle gateway may elect which wireless links to use via its local wireless adapter to communicate within the vehicle.

The scan or test of radio frequency links may be conducted by the context aware radio resource management system. As a first measure, received signal strength and bandwidth availability for a service provider and a protocol are determined. Then a test of radio frequency data capacity is made. This can test upload and download performance for each service provider and protocol. For example, a standard test data volume may be sent via a wireless link to a server location at the service provider. Similarly, a test data volume may be received from a server location by the mobile information handling system via the wireless link. Latency of response, upload and download speed or throughput can then be measured for the service provider and protocol. The data is associated with a location zone and stamped with a time and date. The type of transmitter/receiver or mobile information handling system may also be recorded. This data set provides a wireless link radio frequency profile that may become part of a mobile wireless traffic report. Upon measuring this data for a location, the report may be shared or published by the context aware radio resource management system from the mobile information handling system.

Once a radio frequency channel performance profile is submitted to the context aware radio resource management system and a wireless link selected, the mobile information handling system may periodically scan multiple wireless links or measure the selected wireless link at step 670. The system may conduct testing to determine the capacity of a link during operation. In order to minimize radio communication and use of resources, the network broker may be used to proactively notify a mobile information handling system if a wireless link selection was made using an obsolete crowd-sourced data source. This network broker server system may compare time stamps of crowd-sourced data used for wireless link selection or ranking with current time stamps of network-stored crowd-sourced material.

Testing is similar to the testing described above. Additionally, context aware radio resource management system may assess the quality of the wireless link being used. In addition to the capacity above, metrics such as bit error rate (BER) and signal-to-interference metrics may be assessed. Bit error rate is the ratio of error bits to total bits sent across a wireless link. It is a metric illustrating a signal to noise ratio which can define the quality of a radio connection for a wireless link. A bit error rate may be a comparison of a sent test stream of data by a transmitter with what is received by a receiver. The bit error rate can be tested by a bit error rate tester in software which transmits a known bit pattern to or from the mobile information handling system. Pre-error correction errors are counted. A signal-to-interference ratio may also be measured. Such a measurement is based on the power levels for signal transmission (e.g., per bit) relative to interference levels in the received signal. Packet error rate, signal-to-noise measurement, or other signal quality testing is also contemplated.

At step 680, the periodic wireless link scan updates a wireless key performance indicator (KPI) data matrix stored on the mobile information handling system. The KPI matrix establishes the spatial-temporal radio frequency profile and comprises the data for the mobile wireless traffic report. The updated data is time or date stamped to establish its freshness. The system may repeat the periodic wireless link scans and update the KPI matrix for future intervals of time.

At step 690, the spatial-temporal radio frequency profile of the current mobile wireless traffic report and any associated confidence of estimate may optionally be advertised to the central server database for use by other mobile information handling systems or by devices such as the smart vehicle gateway of the present disclosure. Thus, the mobile information handling system or smart vehicle gateway may provide its contribution to the crowd sourcing data for a time and location of wireless link access. Alternatively, the mobile information handling system or smart vehicle gateway may store the mobile wireless traffic report locally and respond to requests from a central server database for the information.

FIG. 7 shows a method 700 for profiling link energy consumption for wireless communication links. This is an alternative embodiment to the method of FIG. 7 for assessing spatial-temporal radio frequency profiles for wireless links. In addition to assessment of link capacity and quality as in the method of FIG. 6, the system additionally assesses data for device energy consumption relating to various services. In this embodiment, the context aware radio resource management system prepares and delivers an energy link consumption report. The energy link consumption report provides data on power consumed by a mobile information handling system or a smart vehicle gateway while performing certain tasks on a wireless link at a location. Energy link consumption reports contain data indicating how many joules of energy are consumed during sending SMTP emails, sending SMS messages, conducting voice communications, accessing internet services, streaming audio or video, or other uses of mobile information handling systems. This data amounts to another key performance indicator (KPI) in addition to capacity or link quality data for a wireless link. The context aware radio resource management system can measure and utilize some or all data such as link capacity, link quality, and energy consumption in determining preferred wireless links. Link ratings may be calculated similarly to the above description using link energy consumption data. If energy consumption data is unavailable however, the system will function with the mobile wireless traffic reports described in FIG. 6.

Scans for energy consumption information are described further below. The energy link consumption reports retrieved or compiled for the method of FIG. 7 may also record the specific type of information handling system in one embodiment. With a large number of available reports, for example crowd sourced data, filtering for tailored energy consumption information based on a make and model of a mobile information handling system may better account for model-specific variations in wireless operation. As before, the energy link consumption reports are location specific and time specific. Radio frequency scans and energy consumption measurements may consume resources, thus the method begins by searching for available link energy consumption reports.

In step 710, the context aware radio resource management system of a mobile information handling system or smart vehicle gateway may scan for energy link consumption reports for the device and location of operation. The scan searches for fresh, crowd sourced energy link consumption reports among data available from a central server database in the wireless networks 140 and 150 or located elsewhere. As described above, crowd sourcing of energy consumption information will enhance the availability of accurate and current data for locations and times of mobile information handling system operation. A request for a fresh energy link consumption report may be submitted to the central server database or some other database storing such reports. The request may be location specific, time period specific, device specific or any combination of the above. Freshness may be a parameter defined by timestamp data on a report submission. For example, reports submitted for a location on the same day or within the past 24 hours may be qualified as fresh. Any limitation of time on freshness may be used. If a time period during a day is split up on an hourly basis, a fresh report may be one that was submitted within the current or previous hour of the same day. Although a different time period for recent radio frequency traffic in a location may also be used by the present embodiment.

In an alternative embodiment, data and reports may not be stored at a central server database, or only a subset of available data may be stored there. The context aware radio resource management system may make a request for a recent energy link consumption report from similarly situated mobile information handling systems at the same location. This request may come indirectly via a request from the central server. Whether via reports stored in the central database or via a recent request for fresh crowd sourced energy link consumption reports, a scan for pre-existing reports may avoid the need for the mobile information handling system or smart vehicle gateway to conduct an energy consumption survey itself.

Additionally, in some embodiments, the smart vehicle gateway may operate a context aware radio resource management system and maintain energy link consumption reports for local wireless links within a vehicle for connections with mobile information handling systems in a vehicle or IoT devices and sensors within the vehicle. These energy link consumption reports may be used by the context aware radio resource management system to determine optimal downstream local wireless links for the mobile information handling systems or IoT devices and sensors within the vehicle with the smart vehicle gateway.

As with the wireless traffic reports for certain locations, part of the crowd sourcing of energy link consumption report data may also involve performing a hysteresis analysis on the data. Analyzing data from multiple mobile information handling systems may determine trends in wireless link selection happening at a location. If many mobile information handling systems at a location begin to select one preferred wireless link, that link may slow down and underperform. The energy link consumption reports account for this crowding onto a link with the hysteresis analysis. If a large number of users begin to select a given wireless link, then the method for generating energy link consumption reports accounts for this factor. The method may alter which links are recommended or in what order they are recommended.

At block 720, the method may determine that a fresh energy link consumption report is available for the location of the mobile information handling system or smart vehicle gateway. It does so by receiving an acknowledgment or a fresh energy link consumption report from a central server database. At 730, the method assesses the fresh energy link consumption reports. The method may also retrieve and include historical energy link consumption reports, if available. Historical energy link consumption reports may be stored locally for the mobile information handling system or received at the mobile information handling system from a central server database. These historical reports may not meet the freshness limitation, but may prove useful. Although historical reports may not be weighted as heavily as a fresh report, the historical reports may still add value or depth to the data available for a given location and time.

Assessment of energy link consumption reports are used to suggest a wireless link at step 730. If conservation of battery power is a preeminent consideration, a link having the least power consumption for wireless services may be recommended. In embodiments where a weighted list of available links is provided, selection by least-power-consumed on average may be used. The context aware radio resource management system may also utilize user profile information to recommend links based on the most likely used wireless service or combination of services at a location or during a time period. The links having the least power consumption for a heavily used service or services by the mobile information handling system will be recommended.

Least-power-consumed may not always equate with recommending a wireless link with the greatest capacity or quality however. Although less energy consumption often tracks the quality of a link, link quality may vastly improve as greater power is used in transmission. For example, higher power consuming transmission may be used by a transmitter to improve signal to noise ratio and, therefore, more power yields a higher quality link. In this case, the higher power transmission may be preferred. In certain embodiments, detection by a mobile information handling system of the battery state may determine the priority used. In that case, the context aware radio resource management system analyzes the energy link report in combination with a battery power level assessment in determining recommended wireless links. In another alternative, the mobile information handling system may detect connection to an AC power source to set the priority relating to energy link consumption versus radio frequency capacity and quality. Thus, the context aware radio resource management system elects a wireless link based at least in part on the mobile information handling system power consumption assessment and other factors as described in FIG. 3.

If no fresh energy link consumption reports are available at step 720, the method seeks stored historical energy link consumption reports from the central server database at step 740. Depending upon the age of these historical energy link consumption reports and the estimated confidence associated with that data, the method will establish a mobile information handling system power consumption assessment, or that of a smart vehicle gateway, on historical energy link consumption reports stored locally or received locally at step 750. A link may be recommended based upon that report. Similar considerations to the above may be taken into account.

If there are no historical energy link consumption reports recent enough to base an assessment upon, the context aware radio resource management system initiates a mobile information handling system energy link power scan to collect data regarding possible wireless links at step 760. Conducting this energy link power scan consumes power and processor resources, however it provides up-to-date information for a new energy link consumption report. Based upon this new energy link consumption report, the system provides a mobile information handling system power consumption assessment to be used to select a wireless link by the context aware radio resource management system.

A scan or test of radio frequency and energy consumption of links may be conducted by the context aware radio resource management system. Some measures are similar to the method of FIG. 6 to generate a radio frequency link profile. As a first measure, signal strength and bandwidth availability for a service provider and an available protocol is determined. Then a test of radio frequency channel capacity is made. This can test upload and download performance for each service provider and protocol. For example, a standard test data volume may be sent via a wireless link to a server location at the service provider. Similarly, a test data volume may be received from a server location by the mobile information handling system via the wireless link. Latency of response, upload and download speed or throughput can then be measured for the service provider and protocol. In addition, the context aware radio resource management system may measure the energy consumed in transmitting or receiving the test data volume. The power consumed may therefore be expressed in Joules or converted into a Joules/bit or Joules/byte value based on the standard test data volume. The data is associated with a location and time and it is time and date-stamped. The type of transmitter/receiver or mobile information handling system may also be recorded. This energy consumption data may be included in a wireless link radio frequency profile and become part of a mobile wireless traffic report. Upon measuring this data for a location, the report may be shared or published by the context aware radio resource management system from the mobile information handling system.

Once a mobile information handling system power consumption assessment is submitted to the context aware radio resource management system and a wireless link selected, the mobile information handling system may conduct an ongoing mobile information handling system power consumption scan for the wireless link or links being used. Similarly, this power consumption scan may be conducted for a smart vehicle gateway or devices within a vehicle communicating via a smart vehicle gateway. The context aware radio resource management system periodically measures time, location, radio frequency profile data and energy link consumption data for the selected wireless link or links at step 770. The data may be measured during operation of the mobile information handling system. Radio frequency profile measurements such as signal level, capacity, and signal quality may be measured in accordance to the description above for FIG. 6. Power consumption measurements for the mobile information handling system communications on the wireless link are also measured.

Power consumption measurements may be conducted that are specific to the mobile services or data types throughput a smart vehicle gateway used. For example, energy consumption during voice communications may be measured. The amount of power, for example in milliwatts or Joules, may be expressed as a measurement per voice minutes consumed. Power measurements of a radio frequency subsystem from the start of a conversation to the end of a conversation may be measured as described above. The context aware radio resource management system associates this power consumption measurement with the service being utilized. Similarly, for data transferred during internet accesses, power consumption may be measured relative to data volumes uploaded or downloaded. The power would be measured at the active radio frequency subsystem beginning during a download and recording the amount of data or time of a download as well. A power-per-byte or similar measurement may be recorded in an energy link data matrix for that location and time of an internet access. Alternatively, power consumption measurement may be made in terms of number of internet accesses or a combination of accesses and data volumes downloaded or uploaded. Since the power measurements themselves consume power and resources, a sampling of power consumption is more likely. Then estimations of power consumption may be made during operation with a given wireless link for a service type.

In another example, audio or video streaming power consumption may be measured in terms of streaming minutes or data volume. Again, the radio frequency subsystem power consumption may be sampled during the duration of a streaming session and averaged or estimated for the streaming event. The content aware radio resource management system may also measure power consumption levels for SMTP, SMS, or other messaging. This may be done on a per data volume of the messages or based on the number of messages transmitted.

In an aspect, similar power consumption measurements to the above may be made of upstream mobile information handling systems or IoT devices or sensors within a vehicle. These power consumption measurements may be used with a context aware radio resource management system to determine which local wireless links with a smart vehicle gateway may be optimal from a power consumption standpoint for those devices. The power consumption measurements may also be used to determine for each mobile information handling system whether a smart vehicle gateway local wireless link should be used or whether there an external wireless link should be assessed instead.

All of these measurements are then recorded and stored in the radio frequency and power consumption profile as energy link matrix data. This information may be referred to as a link energy consumption report or it may simply be part of a radio frequency profile in a mobile traffic report.

At step 780, the data from the periodic mobile information handling system power consumption scan is updated in an energy link data matrix stored on the mobile information handling system. For the given periodic scan interval, the context aware radio resource management system updates the energy link report matrix in the radio frequency profile. The energy link report matrix establishes the spatial-temporal mobile information handling system power consumption profile. The updated data is time or date stamped to establish its freshness. The system may repeat the periodic mobile information handling system power consumption scans and update the energy link data matrix for future intervals of time. Because measurement scans of this type may be costly in terms of resources and energy consumption, the frequency of such measurements may be limited by the context aware radio resource management system on the mobile device. In one embodiment, depth of wireless link data for statistical purposes at a given location and time may be achieved with crowd sourcing efforts.

At step 790, the spatial-temporal power consumption profile of the mobile information handling system and any associated confidence of estimate may optionally be advertised to the central server database for use by other mobile information handling systems or smart vehicle gateway systems. Thus, the mobile information handling system or smart vehicle gateway may provide its contribution to the crowd sourcing data for a time and location of a wireless link access. Alternatively, the mobile information handling system or smart vehicle gateway may store the mobile wireless traffic report locally. It may optionally respond to requests from a central server database with the radio frequency and wireless link power consumption profile information or reports.

FIG. 8 shows an embodiment example method for determining an optimized wireless link selection for a mobile information handling system during future movement. The mobile information handling system may be a mobile gateway device such as a smart vehicle gateway or smart personal connect gateway in some examples Multiple estimations and data inputs may be analyzed by the context aware radio resource management system. The context aware radio resource management system determines an optimal wireless link selection for a mobile information handling system in terms of cost, power consumption, or quality of wireless channel links for connecting voice or data. FIG. 8 illustrates input data generated via a path prediction system 805, a usage trend determination system to determine predicted path radio usage requirements 810 from user profiles, and a radio link QoS assessment system to determine estimated QoS scoring for radio channels 815 at future locations along the predicted path. Additional inputs may also influence the context aware radio resource management system determination of optimized wireless link selection. Data inputs may include goals or priorities 825 indicating desired priorities of cost, radio link profiles (such as QoS), or power usage from energy link consumption profiles. Additional data analyzed may include current operating states of the mobile device 830. Limitations may also be placed on the context aware radio resource management system to prevent the benefits of the optimized wireless link selection system from intruding too much on user experience. For example, switching too much may impact the efficiency benefits of the method. An example embodiment includes inhibitors 845 that limit changes to wireless links to prevent switching.

The context aware radio resource management system utilizes a path prediction system 805 to determine a predicted future path of travel for the mobile device such as a mobile gateway device during a set of future time intervals. The mobile path prediction system uses position data for the mobile information handling system. Velocity and acceleration data are detected by motion sensors the mobile device or on the vehicle of a smart vehicle gateway in one aspect. Velocity and acceleration may also be determined from position data or changes in position data in other aspects. Position data may be determined via a global positioning system. Alternatively, a mobile positioning system via the wireless network may determine position and movement of the mobile information handling system. Using this position data, the mobile path prediction system estimates a predicted future path of travel for mobile information handling system. In an example embodiment, the mobile information handling system is a smart vehicle gateway or other mobile gateway device travelling in a user area.

With the predicted future path, the flow proceeds at 810 to predict path radio usage requirements for the mobile information handling system based on context and history of usage. At 810, the context aware radio resource management system applies usage trends for a matrix of locations to the predicted future path. The usage trend determination system defines a predominant service profile of expected usage types for locations on the predicted path according to historical trends of usage as recorded in user profile data at those locations. This may include expected usage types for a mobile information handling system such as a smart vehicle gateway. In another aspect, expected usage types for information handling systems or IoT devices in a vehicle may also be used to define a predominant service profile.

The flow proceeds to 815 where a radio link QoS assessment system utilizes a user service matrix of spatial-temporal radio frequency profiles for locations to determine estimated QoS scoring for radio channels 815. By applying the predicted future path from 805, the radio link QoS assessment system predicts radio link quality over the predicted user path for a variety of radio connections that may be suitable for the mobile device. Wireless intelligence reports are used to create radio link profiles for locations. Radio link profiles may include data relating to mobile wireless link traffic and service provider link ratings. Radio link profiles of wireless radio links may also include QoS parameters of data latency, jitter of signal, packet loss statistics, and bandwidth. Minimum QoS requirements of the mobile device will be partially determined by the expected usage at the locations along the predicted path.

The flow proceeds to 820. At 820, the system applies optimization policies for radio link selection. Optimization policies may blend considerations such as cost of radio link usage, power consumption of the mobile system to use the radio link, or quality of service considerations. These optimization policies are applied to the determined predominant service profile of expected usage types and radio link quality over the predicted future path locations. Additional inputs into the optimized radio link selection at 820 may include factors set by the user at 825. Factors set by the user may relate to cost, power usage, and QoS selections.

Sensors may provide additional automatic inputs into the optimized radio link selection at 820 as well. Sensors may detect mobile device current state information at 830 which may include battery levels, the current radio wireless link operating on the mobile device, and consideration of the most current mobile device location. Such incumbent current state information will be considered to determine if making a change to different radio wireless connection is worthwhile. Additionally, the incumbent current state will be weighed with the risk of service interruption or other factors such as whether the improvement is worth the change. For example, if the currently active radio link is determined to be one of the top few optimal radio links on the predicted path, the context aware radio management system will elect not to switch radio links even if the current link is not the most optimal from a cost, QoS, or power perspective. Alternatively, if the weighted QoS parameters of the currently operating link are meet the minimum requirements or are within a threshold level of deviation from the most optimal radio link, then no switch of radio link will occur.

After application of the optimization policies at 820, the flow proceeds to decision diamond 835 for the application of a location validation filter 835. The location validation filter determines whether the predicted future path for the mobile information system is still accurate based on the most recent location measurement. If a sudden change in location and trajectory are detected that deviate from the predicted future path at this stage in the method, the location validation filter rejects the predicted path. Upon rejection, the flow returns to 805 to repeat the process of predicting a future path for the mobile device. If the location validation filter detects a recent location measurement still in or near the predicted future path, this validates the predicted future path and the flow proceeds to 840.

At 840, the method applies the changes to radio wireless link or radio band on the mobile device. Inputs are received at 840 that may override the radio link change command. For example, override commands set by a user or data service provider (not shown) may restrict application of a change to the wireless link used by the mobile device. Inhibitors 845 may also alter implementation of the command to switch wireless links. Inhibitors 845 are based on several factors including time of progression through the predicted future path, locations, and QoS considerations. These inhibitors 845 are used to inhibit highly frequent wireless link changes that could actually cost the mobile device system rather than improve performance. Additional inhibitors 845 include restrictions based on location, such as if the mobile device is in an airplane or at a work location where limitations may be placed on wireless link options. For example, an airplane may be restricted to a WI-FI access. An office location may have restrictions for organization-issued mobile devices limiting wireless link access to WLAN access for cost or security reasons while the mobile device is on the organization's premises. If an override command or the inhibitors 845 do not override the changes, then the context aware radio resource management system executes commands to make the wireless radio link change.

FIG. 9 shows method for predicting future mobile device path locations such as with a mobile path prediction system as part of the context aware radio resource management system. The method begins at 905 where the mobile path prediction system determines the position of a smart vehicle gateway or a mobile device. The mobile path prediction system may operate via execution of instructions via a processor on the mobile information handling system or via a processor on one or more information handling systems in communication with the smart vehicle gateway via a network connection. For example, the latter may be a cloud based context aware radio resource management system. The mobile information handling system position is detected. To determine velocity, acceleration and direction, an extrapolation of multiple measured position data points may be used. For example, multiple position data points may be taken to determine direction, velocity and acceleration such as via a global positioning system. Alternatively, a mobile positioning system using radio signals strength and location measurements with respect to one or more cell tower locations via a wireless network may determine mobile device position as is known in the art. In an alternative embodiment, a mobile device such as a smart vehicle gateway or the vehicle in which it operates may have motion sensors integrated to determine direction, velocity, and acceleration. With the motion sensors, at least one position data point is needed. Example motion sensors include geomagnetic reference sensors and any combination of accelerometers and gyroscopic sensors. The position data and any detected velocity and acceleration data is reported to the context aware radio resource management system.

The flow proceeds to 910 where the mobile path prediction system projects multiple probable trajectories for the mobile device(s) at future time intervals. This is done via extrapolating position, direction, velocity and acceleration to a plurality of future time intervals. The locations at the plurality of time intervals establish trajectory paths. Multiple trajectories are determined so that for each future time interval there is a plurality of possible future locations. Proceeding to 915, the mobile path prediction system of the context aware radio resource management system applies probability statistics to the multitude of future path locations. In the present embodiment, a linear mean square estimation is applied to the determined trajectory locations and less probable path locations are discarded. In an example embodiment, the path prediction system applies a Kalman filter probability estimation to the probable trajectory locations to filter out lowest probability path locations.

The flow proceeds to 920, where the mobile path prediction system determines a predicted preliminary path for several locations corresponding to upcoming intervals of time. This preliminary predicted path is then mapped to a bin map depicting a user area at 925. In an example embodiment, the bin map includes a grid of latitude and longitude coordinates for future mobile device path locations. Such an example is illustrated further below with respect to FIG. 9A.

Proceeding to 930, the context aware radio resource management system accesses a location matrix having historic visitation data for the mobile information handling system. The history of visitation is recorded from user profile data for mobile devices as described above. The visitation history location matrix may be also mapped to a bin map of user area. The visitation history location matrix contains data about the frequency and time spent at locations and may also include temporal information relating to times during the day when such visitation is made. In this way, the visitation history location matrix will contain information relating to cyclostationary daily habits of a mobile device user's visitation.

The context aware radio resource management system proceeds to 935. At 935, the path prediction system portion correlates the preliminary predicted path with the visitation history location matrix. The preliminary predicted path begins as a selected mobile device trajectory. In the example embodiment, this may be done via overlay of bin maps containing both preliminary predicted path and the visitation history information for locations near the preliminary predicted path. An example visitation history matrix bin map is shown in FIG. 9B below. Nearby locations for the visitation history matrix may be limited to those locations that fall within a certain number of bin map grid boxes from the preliminary predicted path. How many bin map grid boxes are used as nearby locations will depend on the physical size of each bin map grid box and factors such as how many future time intervals are used to determine the predicted future path.

The flow proceeds to 940 where the path prediction system modifies the preliminary predicted path locations based on the visitation history location matrix data. The path prediction system modifies the preliminary predicted path to include a location on the visitation history location matrix depending on the frequency of visitation to that location. Additional factors in modifying the preliminary predicted path may include the nearness of the frequently visited grid map box location to the preliminary predicted path. For example, a highly visited location one grid box away from the preliminary predicted path will cause a modification of the preliminary predicted path. However, a less visited location three or more grid boxes away from a preliminary predicted path location will unlikely cause a modification to the preliminary predicted path. The mobile path prediction system sets a threshold of factors to determine at what point the modification to the preliminary path will occur. Application of a set of conditional probabilities, such as with Bayesian classifier statistics, may take into account several variables such as proximity to trajectory and frequency of visitation to determine where to predict future path locations. Another factor having impact on modifying the preliminary predicted path includes the time of day. Time of day takes into account cyclostationary considerations such as daily routines of the mobile device user. The modification of the preliminary predicted path may occur in a recursive fashion to correlate additional probability estimation of a location along the preliminary predicted path until a predicted future path is determined.

In one embodiment of the application of determining the predicted path is based on probability that a mobile information handling system visits a location during a daily time interval. By way of example, probability of visiting a location may be determined as follows: Probability of visiting a location(x _(i) ,y _(i) ,t+interval)=Historical Probability of visiting(x _(i) ,y _(i) ,t+interval)*Normalized Distance Computation, where Normalized Distance Computation=1/SQRT(2π)*exponential(distance of a location from a preliminary path location); and where Distance=[Places Historically visited(xi,yi,t)−Predicted Location utilizing a Kalman Filter(t+interval)]²/σ².

x_(i), y_(i)=potential locations visited during prediction interval

σ=variance in location prediction,

Interval=mobile prediction path time period.

The mobile path prediction system selects a path x_(i), y_(i) with the highest Bayesian posterior probability given the preliminary predicted path. Of course, other probability computations are also contemplated using distance from the preliminary predicted path and history of visiting a location in the user area.

At 945, the mobile prediction path system establishes the selected predicted path over the future time intervals including modifications from 940. Proceeding to 950, the mobile prediction path system applies the modified predicted future path to the bin map of the user area for the smart vehicle gateway. This predicted future path and bin mapping is used by the context aware radio resource management system at later phases of radio link selection.

FIG. 10A depicts a bin map 1000 with example latitude 1005 and longitude 1010 coordinates upon which several types of information may be set over the bin map by the context aware radio resource management system. The grid boxes 1015 on the bin maps 1000 of FIGS. 10A, 10B, and 10C, may determine the granularity with which a location is defined. In the example embodiments of FIG. 10A and FIG. 10B, the grid squares represent approximately one half kilometer by one half kilometer. The overlay grid square information may include locations on the bin map. Locations such as home and work 1030 are shown in FIG. 10A. Locations that may represent predicted start and end locations are shown as 1055 in FIG. 10B. Locations 1020 are shown with a predicted future path 1022 in the example embodiment of FIG. 10C.

FIG. 10A depicts historic visitation matrix information in an overlay of grid squares 1015 on the bin map 1000 in one example embodiment of the present disclosure. Such grid squares identify locations visited by the mobile device such as a mobile gateway device or mobile information handling systems. A third dimension, pattern or color indication (not shown) may be used to show frequency of visitation or ranges of visitation frequency applied to locations 1015 on the bin map 1000. Additional data may reflect the smart vehicle gateway or mobile device requirements at locations 1015. The smart vehicle gateway or mobile device requirements reflect expected wireless service type usage at the visited locations 1015. Again, colors or patterns or a third dimension on grid boxes 1015 may be used on the bin map 1000 to show predominant usage expected at grid box locations on the bin map 1000.

The example embodiment of FIG. 10B may show a bin map 1000 having estimated QoS levels for the variety of wireless links available at grid box locations 1015. As with the other bin maps, colors, patterns or a third dimension on the grid boxes 1015 may be used on the bin map 1000 to show QoS ranges or energy consumption data for wireless link sources at depicted grid box locations. In the shown example embodiment, hatching is depicted highlighting certain grid box locations. In some example aspects, this hatching may indicate great wireless connectivity. On other example aspects, the hatching may instead represent medium or poor wireless QoS or wireless connectivity levels. Similarly in other aspects, the hatching may represent wireless link energy consumption levels predicted. The measured QoS data and energy link consumption data is from a plurality of wireless intelligence reports for locations in the user area. Grid boxes 1015 may indicate in some aspects the locations in a user area where sufficient crowd-sourced mobile wireless traffic report data is available at the context aware radiofrequency resource management system.

In the example embodiment of FIG. 10C, an example embodiment of a bin map is shown with a predicted future path 1022 overlaid on grid boxes 1015 showing levels of wireless connectivity, including wireless conditions, for locations along the predicted future path. Legend 1025 depicts an example embodiment of crowd-sourced data available for the predicted future path. Bold grid boxes 1026 indicate locations along the predicted future path 1022 determined by the mobile path prediction system described in embodiments herein. Grid boxes 1015 may also indicate wireless connectivity conditions.

Great wireless conditions are indicated for locations with highlighting such as 1027. Good wireless connectivity conditions are indicated for locations with highlighting such as 1028. Poor wireless connectivity conditions are indicated for locations with highlighting such as 1029. These classifications of wireless connectivity conditions are only an example embodiment of classification of wireless conditions along a predicted future path. It is understood that multiple or fewer classification levels may be used instead. Wireless connectivity levels may be determined based on link ratings for wireless links as in the present disclosure. Thresholds of connectivity conditions may be set according to link rating levels. For example, link ratings at locations such as those depicted in FIG. 10B or 10C, will be drawn from crowd-sourced wireless traffic reports. Descriptions for determination of link ratings according to the present disclosure will rely in many embodiments on one or more QoS parameters as disclosed in several embodiments herein. The link ratings in the presently described embodiments appear as a percentage out of 100%, although any scale will work. A threshold level of wireless link rating may be used in determining a classification of the wireless link based on its expected wireless conditions. Multiple wireless link rating thresholds may define several classifications for wireless conditions at locations such as depicted in FIG. 10B and FIG. 10C.

Although wireless conditions and link ratings may include QoS levels for one or more wireless links selected for a location along a predicted future path in one example embodiment, other factors may influence wireless link ratings in several described embodiments. Accordingly, the classification of wireless conditions at locations may similarly be impacted by these factors. In one example embodiment, wireless conditions may also represent energy link consumption levels for wireless links. Energy link consumption reporting from crowd-source data may be used to weight the wireless link ratings. The weighting of QoS parameters is based on power consumed to transmit or receive data or communications. The weighting applied may also be variable and dependent on the power state of a mobile information handling system. For example, energy link consumption levels may have a greater or lesser impact on link ratings depending on battery power state levels or power sources available to the mobile devices. A mobile information handling system with a battery state of charge at low power will require more careful energy consumption measures. Thus, energy link consumption reports may be utilized with greater emphasis on link energy consumption in determining link ratings.

In yet another embodiment, wireless conditions may also be rated according to some embodiments herein according to cost of data or communication transfers. For example, roaming charges may be incurred for some wireless links. In other examples, some carriers may be substantially more expensive on a cost per byte basis. In some embodiments, link ratings may be weighted by factors including cost of data transfers or communications and accordingly determination of wireless condition classification will similarly be affected. These factors may be dependent on user selection or determination by IT management of mobile information handling systems through user settings and the like.

FIG. 11 shows a method for defining the expected minimum QoS requirements for the smart vehicle gateway or mobile device along a predicted future path. In some embodiments, this may include defining expected minimum QoS requirements for information handling systems or IoT devices within a vehicle or a local network of a mobile gateway device. The present example embodiment describes operation of a smart vehicle gateway, but any mobile information handling system may operate in similar principles. At 1105, the position of the smart vehicle gateway is determined including latitude, longitude and time to identify time and location of the smart vehicle gateway. Proceeding to 1110, the system determines the predicted future path of the smart vehicle gateway in accordance with the disclosures above. An example predicted path on a bin map with grid coordinate system is shown at 1115 similar to FIG. 10A above. At 1120, new data relating to wireless service types are collected by the system in accordance with disclosures above. This usage trend data updates the historical wireless service type profiles as part of the user profiles for locations in the user area at 1125. The user area may be of any scope. It is contemplated that a user area may be worldwide, within a country or state, or within a metropolitan area. In alternative embodiments the user area may be limited to an expected area of usage based on previous visitation history or realistic limits on likely travel in the contemplated future time interval. In a further embodiment, user area may be limited to areas within reasonable distance from the predicted future path of the mobile information handling system. Limiting the user area is helpful to reduce the data set used for analysis.

The flow proceeds to 1130 where the method compares the predicted future path with the historical wireless service type profiles for the locations in or near the predicted future path. This comparison of wireless service types used at locations along the predicted path is averaged and a predominant wireless service type profile is determined over the predicted future path. An example wireless service type profile is depicted at 1135 showing predicted percentages of use of wireless service types based on historical use at the locations in the predicted path. The predominant wireless service type profile for the path may be bounded with data from similar times of day to the predicted future path travel time. Alternatively, historical wireless service profile data may be taken for any daily time intervals such as when wireless service depends more strictly on location rather than time of day.

With a predominant wireless service type profile 1135 of expected usage types for the predicted future path, the method accesses policy settings for QoS requirements for service types. An example policy table for QoS requirements by service type is shown at 1140. The QoS requirements table shows the minimum requirements for various QoS parameters according to wireless service type. QoS parameters for this embodiment include latency, packet loss, jitter and bandwidth (BW). Tolerance levels are shown in this policy table for voice, SMS, streaming data, email, http downloads, ftp downloads, and other wireless data service types. In the shown example at 1140, voice service and streaming applications can tolerate very little latency (<150 msec and <500 msec respectively) or jitter (<50 msec²) whereas SMS and email applications can tolerate substantially more latency (<5000 msec) and jitter (<200 msec² and <500 msec²). For bandwidth (BW) however, voice requires comparatively low bandwidth (>30 kbps) similar to SMS and email, whereas streaming requires comparatively high bandwidth (>1000 kbps). With the predominant wireless service type profile 1135 for the predicted path determined, the method applies the QoS requirements policy at 1145. Proceeding to 1150, the minimum QoS requirements are weighted according to expected wireless service usage percentages over the predicted path. This may be achieved by multiplying the percentages of the wireless service type profile 1135 as weighting factors to the QoS requirements policy shown as 1140. In another embodiment, the method selects one or more predominant wireless service types that are expected to be used and selects for QoS criteria from the policy table 1140 based on the predominant wireless service type or types. By way of example in FIG. 11, the predominant wireless service types would be voice and SMS. At 1135 and 1140 the expected predominant usage would require relatively low latency (due to prominence of voice and to a lesser degree due to streaming), low packet loss (due to prominence of voice and SMS), and low jitter (due to voice). However, the QoS requirements would also only require a relatively low bandwidth (due to prominence of voice and SMS, and to a lesser degree email usage). A usage trend determination system of the context aware radio resource management system performs the above described method. In another example, predominant wireless service types could be an IoT device data transfer to a remote data center. This expected predominant usage may tolerate greater latency and jitter, but not packet loss. QoS requirements however may require relatively low bandwidth due to the nature of the data transfer.

FIG. 12 shows a method of predicting radio link quality of service, power consumption, and costs over the predicted user path for a plurality of radio connections. The method also shows utilizing the predicted radio link quality, cost and power consumption to select an optimized radio link and create a policy for linking or switching radio links for the smart vehicle gateway or for one or more mobile information handling systems or IoT devices. A wireless link QoS assessment system of the context aware radio resource management system utilizes a radio link matrix of spatial-temporal radio frequency profiles with QoS data collected for a plurality of locations in a user area. This radio link matrix data is used to determine estimated QoS scoring for radio channels at locations in the user area. An example of such a radio link matrix of radio link quality measurements is shown in FIG. 10C as depicted on a bin map of a user area.

At 1205, the position of the user is determined including latitude, longitude and time to identify time and location of the smart vehicle gateway. Proceeding to 1210, the system determines the predicted future path of the information handling system in accordance with the disclosures above. At 1215, new data relating to radio link measurements according to location according to positional latitude and longitude may be collected from a plurality of mobile devices such as other smart vehicle gateways. The context aware radio management system may include crowd-sourced data for radio links or channels of radio connections. The new radio link measurements may also include a time component so radio service levels and quality of service may be measured according to time of day. Such collection of radio link measurements may be conducted in accordance with methods and systems described above.

At 1220, the radio link measurements are included in the matrix of radio link measurements according to location, time, and even type of radio service. Types of radio links can include a type of wireless link, such as Wi-Fi/WLAN link or macro-cellular wireless links such as 2G, 3G, 4G and similar future wireless links. Types of radio links may also include macro-cellular wireless links from a variety of service providers. Proceeding to 1225, the predicted radio link quality for a plurality of wireless links and service providers is determined over the predicted future path of the smart vehicle gateway. This anticipated radio link QoS includes QoS parameters for locations along the predicted future mobile information handling system path. Additionally, average QoS parameters for all locations along the predicted future path may be determined for the wireless link types.

The flow proceeds to 1235 where the method compares predicted radio link quality for the predicted future path of 1225 with the minimum mobile device QoS requirements 1230, such as determined according to the method embodiments of FIG. 11. At 1235, the wireless link QoS assessment system matches the parameters for the minimum mobile device QoS requirements 1230 in the predicted future path with the predicted QoS parameter levels for a plurality of radio links. In this way, an optimized radio link selection may be made at 1235 by the context aware radio resource management system. In the embodiment of FIG. 12, QoS of the radio link has been described as the overriding consideration for selection of an optimized wireless link at 1235.

Other factors may also be considered however in additional embodiments. State of the device and user-desired settings may also be assessed in selecting the optimized radio link at 1235. For example, the state of the battery level or access to a power source may be detected. Limited power supply levels may influence selection of a wireless link based on energy link consumption measurements as stored with the radio link matrix of spatial-temporal radio frequency profiles and used to predicted power consumption levels for the predicted future path of the mobile device. Additionally, cost of wireless link access may be considered when selecting a wireless link as described in the current disclosure. For example, cost of access to macro-cellular wireless links may vary widely among wireless service providers. In another example, a Wi-Fi wireless link may be a free option that would be preferred by the user over a macro-cellular wireless link. In another example embodiment, switching to a wireless link on a wireless service carrier for which an IMSI is readily available to establish a home network connection may avoid roaming charges or other inefficiencies. In another example embodiment, with an eSIM having several IMSI options available, cost of data differences between two competing wireless service carrier home networks may be considered.

The flow proceeds to 1240, where the context aware radio resource management system creates a radio link selection policy based on the user location and usage context. This radio link selection policy may be transmitted as a recommendation to the user in one embodiment. In another embodiment, it may create a wireless link switching command subject to inhibitors or override settings as described above in FIG. 8.

In an alternative embodiment, locations or wireless links that cannot meet the minimum QoS requirements of the expected wireless service type usages by the may be designated by the context aware radio resource management system as dead zones, at least for a particular type of wireless link is unavailable or of such low QoS that it cannot be effectively used. These dead zones may also become part of the radio link selection policy as wireless links to be avoided.

FIG. 13 shows an example embodiment method for operation of a mobile information handling system operating as a mobile gateway device. The mobile gateway device transmits and receives data and communications on behalf of a local mobile gateway network. Several devices may be connected through to a wider network connection such as a broadband connection via the mobile gateway device. The mobile gateway device may support local gateway network connections for devices including a plurality of mobile information handling systems and IoT devices for example. In example embodiments, the mobile gateway device may be a smart vehicle gateway as described above or a smart personal connect gateway in some instances. Accordingly, the smart vehicle gateway may have a local vehicle network of devices for which it serves as a gateway in one embodiment of a mobile gateway device. A smart vehicle gateway may also have access to power supply sources from the vehicle. A smart personal connect gateway may serve a local personal area network of devices within a vicinity or WPAN range of the smart personal connect gateway in another embodiment. The smart personal connect gateway may have limited access to power supply and may rely on battery power enhancing consideration of wireless link energy consumption in an aspect.

The mobile gateway device utilizes a context aware radio resource management system with a path prediction system. Further, a smart connection manager for the mobile gateway device operates with the context aware radio resource management system. The smart connection manager manages the local wireless adapters and the upstream-facing wireless adapters for the mobile gateway device. In the present disclosure, the smart connection manager, using the context aware radio resource management system, will determine and establish an optimized wireless link for a location in a predicted future mobile path. The smart connection manager may establish the selected optimal wireless link for a WWAN connection on any wireless service carrier as a home network. The mobile gateway device will have one or more programmable eSIMs available for switching between available IMSIs for the upstream wireless adapters in some embodiments so several wireless service providers may serve as a home network.

In some embodiments, a mobile gateway device is set to establish a plurality of wireless links to a WWAN or WLAN for robustness of wireless data communications across a plurality of locations along a predicted future mobile path. As the vehicle travels, a smart connection manager may determine which of the plurality of wireless links to use for data and communications received from the vehicle local network. The robustness of activating redundant wireless links may be beneficial to ensure at least one wireless link is available at a level of QoS and availability to accommodate communications from a mobile gateway device across the predicted future mobile path. As described above in some embodiments, the context aware radio resource management system may determine a list of optimal wireless links over the predicted future mobile path during a future time interval. A plurality of locations in the predicted future mobile path will be assessed by the context aware radio frequency management system in some aspects. Determinations may be made in accordance with descriptions in FIG. 11 using a predominant service profile and descriptions and with FIG. 12 yielding predictions of radio connections over the predicted future mobile path.

The method of FIG. 13 begins at 1305 where the smart connection manager may determine which mobile information handling systems and IoT devices are functional and operating within a local network to the mobile gateway device. Using a local wireless adapter, the mobile gateway device may poll the local vehicle mobile information handling systems and IoT devices previously operational within a vehicle for example. Alternatively, devices may attempt to pair or transmit to the mobile gateway device local wireless adapter to indicate activity within the vehicle.

The mobile gateway device may scan local wireless links within the vehicle to determine optimal connectivity. An initial scan may determine whether certain local wireless links are available or within working range. For example, a Bluetooth® wireless link may require pairing before it can be used with a mobile information handling system or IoT device. Additionally, the smart connection manager may determine immediate radio frequency conditions or traffic on local wireless links.

At 1310, the context aware radio resource management system may select optimal local wireless network links for each IoT device or mobile information handling system operating in a local wireless network of the mobile gateway device. The smart connection manager of the mobile gateway device may access a context aware radio resource management system either locally or remotely. The wireless device usage trend data and spatial temporal radio frequency profiles may be assessed for the local wireless links with the mobile gateway device. In addition, wireless device usage trend data and spatial temporal radio frequency profiles may be assessed for any mobile information handling systems and the IoT devices within the local mobile gateway network. The smart connection manager may also receive or determine the quality of direct access options for the mobile information handling systems and IoT devices in the vehicle, if available. Direct access may include connections by the mobile information handling systems in the vehicle or in range of a WPAN directly with WWAN wireless links or other wireless links rather than connection through a mobile gateway device. With this information the smart connection manager software will link with a context aware radio resource management system similar to the embodiments described above to determine a list of optimal local vehicle wireless links and external wireless links.

From the above list of optimal local vehicle wireless links and external wireless links, the smart connection manager will select optimal local wireless links. In many instances, the smart connection manager will select a local vehicle wireless link for each information handling system or IoT device within the vehicle. Often, a local link to a mobile gateway device will provide the high levels of wireless signal quality and connectivity compared to a direct external link. In many cases, link to a mobile gateway device will also provide a low power consumption option for mobile devices within the local mobile gateway network such as in a vehicle. In yet another aspect, the function of the mobile gateway device may also provide a lower data cost for transmission and reception of data. For example, programmable eSIM flexibility with respect to selection of home networks may be available for a mobile gateway device but not for devices within the local vehicle network. At this stage, the local wireless adapter of the mobile gateway device will be directed to establish one or more local vehicle wireless links accordingly.

The smart connection manager may also coordinate the upstream wireless adapter to establish one or more wireless links with WWAN macro-cellular networks, such as 150 above. In other aspects, the smart connection manager may also coordinate upstream wireless links with other wireless networks, such as WLAN networks 140 above, if available. The smart connection manager will work in connection with a context aware radio resource management system operating locally, remotely, or some combination thereof.

At 1320, context aware radio resource management system with a path prediction system may determine a predicted future mobile path of the mobile gateway device in accordance with disclosures herein such as that of FIGS. 9-10C. The predictive path system may begin with detection of a current location of the mobile gateway device from a satellite global positioning system or other position detector as described above. The predicted future mobile path for the vehicle or the mobile gateway device may be estimated using probable trajectory estimations at future time intervals. Probability of future path locations may be impacted by map locations indicating roads, train tracks or other avenues upon which a vehicle may likely be travelling. For example, a truck or car will more likely result in future path locations in a projected path trajectory that follows roads or highways in the area. To utilize the cyclostationary patterns that vehicles may travel, access to location matrix histories for the mobile gateway device indicating visitation history may also assist in determination of predicted future mobile path in accordance with the disclosures above. For example, cyclostationary travel patterns along commuting routes or delivery routes may be reflected in the location matrix histories for the mobile gateway device.

Proceeding to 1325, the smart connection manager determines what radio options are available for upstream communication via wireless links. For example, the smart connection manager may scan the available upstream wireless adapter radios for protocols available. This may include one or more WWAN, WLAN, or satellite radio options. Within a wireless adapter such as a WWAN capable adapter, multiple wireless service carrier networks may be detected as available. The scan may also determine an immediate state of various wireless link options. If a satellite radio is available, it may be used in some embodiments to establish a high priority connection with a context aware radio resource management system server for access to crowd-sourced RF intelligence reports. In some aspects, a connection with the context aware radio resource management system server for wireless link assessment capabilities may be used if those operations are not conducted locally. In other aspects, the present method may use any wireless connection currently available to establish links to a context aware radio resource management system server instead of a satellite link.

At 1330, the context aware radio resource management system provides wireless intelligence reports including mobile wireless traffic reports for historical trends and crowd-sourced data on the wireless state of various wireless link options. The various wireless link options may include a WLAN, or may include WWAN wireless service providers and the protocols available from those providers. Spatial temporal user profiles including wireless usage trend data for the mobile gateway device are also accessed at 1335 and provided to a context aware radio resource management system. In one embodiment, the spatial temporal user profiles including wireless usage trend data for mobile information handling systems and IoT devices within the vehicle will also be assessed by the context aware radio resource management system. The volumes and type of data or communications expected, including how transmission may occur, for the information handling systems and IoT devices operating within the vehicle will impact the optimization scoring of wireless links in an aspect of the present disclosure.

At 1340, the context aware radio resource management system will make a determination of optimization scoring for the available wireless links in accordance with disclosures herein. In an embodiment, a future location may be selected from the predicted future mobile path for determination of optimal wireless links to be selected during future travel of the mobile gateway device. For example, a representative location along the predicted future mobile path may be chosen. In other embodiments, a plurality of future locations may be selected along the predicted future mobile path for determination of optimal wireless links to be selected.

A list of optimal wireless link options will be generated for at least one location in the predicted future mobile path for the mobile gateway device. In other embodiments, the context aware radio resource management system according to one or more embodiments described herein will determine a list of optimal wireless link options for the predicted future mobile path based on the plurality of future locations along the predicted future mobile path. For example, the embodiments described in FIGS. 11 and 12 describe an example determination of a list of optimal wireless link options across a predicted future mobile path. The plurality of locations are assessed for wireless link quality in an embodiment and selected accordingly for conditions predicted for the plurality of locations. In another embodiment, link energy consumption may factor into determination of suitability of wireless links at the plurality of locations along a predicted path. In yet other embodiments described, wireless link costs for data transfer at the plurality of locations may be factored into determination of suitability of wireless links at the plurality of locations. In an aspect, the list optimal wireless link options will include determination of available WWAN wireless protocols by wireless service carrier as well as other wireless link options, if available.

The list of optimal wireless service carriers and available wireless protocols will be used to select a wireless link protocol from among the optimal wireless links to establish communication to a WWAN or WLAN. At 1345, a smart connection manager or a context aware radio resource management system may select a wireless link protocol for the mobile gateway device.

Proceeding to 1350, the smart connection manager will determine whether plural wireless links are to be connected. If not, the method may end. If multiple wireless links are to be established then flow may return to 1325 to identify additional radio options. Flow may then proceed as described above until no further wireless links are to be established.

FIG. 14A and FIG. 14B show an example method embodiment for operation of a context aware radio resource management system for scheduling transmission of data based on critical level classification of the data and wireless link radio conditions. In particular, a wireless adapter may schedule transmission of data across a wireless link based on relative predicted QoS link ratings for locations along a predicted future pathway of the mobile information handling system. In an example embodiment, the mobile information handling system may be a mobile gateway device such as a smart vehicle gateway.

Data may be scheduled for transmission by a smart connection manager or a wireless adapter depending on predicted wireless link connectivity conditions for locations along the predicted future path determined for the mobile information handling system. In a further aspect, data of lower criticality may be rescheduled for transmission when a location has poor wireless conditions. The scheduled transmission may be delayed until a location on the predicted future path is encountered with improved wireless connectivity conditions. However, highly critical data may still be transmitted, if possible, regardless of the wireless conditions at a location. In yet another aspect of the present disclosure, data transmission may be scheduled for accelerated transmission if future locations upcoming on a predicted future path will be of poor wireless quality.

The method of FIG. 14A begins a 1402. A list of optimal wireless link options will be generated for the predicted future path. The list will include determination of available WWAN wireless protocols by wireless service carrier as well as other wireless link options, if available. For example, other wireless link options may include WLAN links or satellite links. The list of optimal wireless service carriers and available wireless protocols will be used to select one or more wireless link protocols from among the optimal wireless links to establish communication to a WWAN or WLAN along the predicted future path. In an aspect, this may include determining optimization scoring for a plurality of wireless links to be established for the mobile information handling system. Determination of link ratings for the selected optimal wireless links at locations along the predicted future path may be made in accordance with embodiments described along with FIG. 13. One or more wireless links may be selected based on the link ratings across the predicted future path.

Using the context aware radio resource management system, a determination of optimization scoring for the available wireless links at a several locations along the predicted future path is made in accordance with disclosures herein. In an aspect, the context aware radio resource management system will classify predicted performance at locations along the predicted future mobile path for each of the established wireless links. The predicted performance at the locations is drawn from crowd-sourced wire traffic reports for the location that correspond to the established wireless link or links. With this information, locations along the predicted future path may be classified as to anticipated wireless quality and connectivity conditions for established wireless links. In an example embodiment, two classifications may be used such as one for good conditions and one for poor conditions. An example of locations with two wireless connectivity ratings for good and poor conditions is shown in an embodiment of FIG. 10B. In other embodiments, three or more wireless condition classifications may be used for locations along a predicted future path. For example, FIG. 10C above illustrates classification for locations including great wireless conditions, good wireless conditions, and poor wireless conditions.

Thresholds may be used and applied to link ratings for established optimal wireless links at locations along the predicted future path. For example, a link rating threshold may be used to define good conditions versus poor conditions for an established optimal wireless link at each future predicted path location such as, for example, the embodiment illustrated in FIG. 10B. In another example embodiment, two link rating thresholds may be used for established optimal wireless links to delineate great conditions, good conditions, and poor conditions at each location along a predicted future path as shown in an example embodiment of FIG. 10C. As described further above, link ratings may include a Link Rating (j) having a link rating matrix such as (60%, 80%, 95%, 90%, 95%) for wireless protocols (2G, 2.5G, 3G, 3.5G, 4G) in an example embodiment. Percentage levels may be used to set threshold for the wireless condition classifications. They may be weighted toward certain wireless protocols in some circumstances. For example, 3.5G or 4G communication of data may be more desirable protocols. Thus, a link rating threshold for those wireless protocols may be weighted to have more effect in the wireless condition classification for the established optimal wireless links. The above data is illustrative only. Example threshold levels may include a 4G threshold of 92% and above for great conditions, 65% to 92% for good conditions, and below 65% may define poor conditions at a location in an example embodiment.

The established wireless link classification for locations along the predicted future path enables scheduling of data transmission as described in the present disclosure. Transmission of data under poor wireless conditions may require additional energy and limit the bandwidth available for data having a higher criticality rating. Thus, scheduling data with lower criticality classification to transmit at locations with great wireless conditions may free up bandwidth and increase efficiency during travel along a predicted future path. The thresholds for defining wireless link classifications at locations may serve as levels of minimum QoS as described in the present embodiment. Reaching thresholds or minimum QoS levels determine transmission scheduling depending on data criticality classifications for data to be transmitted. As will be seen, data transmission may be postponed when current conditions at a location are poor in one aspect of the present disclosure. In other aspects of the present disclosure, anticipated poor wireless conditions at future locations along the predicted future path may trigger scheduling of data transmission to be accelerated when better wireless conditions exist at a location.

In accordance with several disclosures herein, link ratings that define wireless conditions as locations along a predicted future path may be impacted by several additional factors in addition to the QoS parameters described in embodiments above. Other embodiments discussed in the present disclosure include the effect of wireless link energy consumption on link ratings as well. If a mobile information handling system relies on battery power, a state of charge may determine whether link energy consumption by wireless links is factored in link rating determinations. As explained in examples above, weighting may be applied to lower power consuming wireless links when determining link ratings. In another example embodiment, cost of data transfer or communication may be established as a weighting factor in determining a link rating for a wireless link at a location. For example, an additional roaming charge at a location may reduce the link rating of a wireless link at a location. These factors are discussed in embodiments above. Link rating determinations relate to wireless condition assessment and classification in the present embodiment. As described above, these wireless condition assessments and classifications may include the factors including link energy consumption or cost of data or communication transfers.

Proceeding to 1404, the smart connection manager may receive data to be transmitted. In a mobile information handling system embodiment, data to be transmitted is queued and prepared for transmission by the wireless adapter. In a further embodiment where the mobile information handling system is a mobile gateway device, data from IoT devices and information handling systems in a local network are transmitted to the mobile gateway device and received their for pass-through transmission controlled by the smart connection manager on one or more established optimal wireless links.

Proceeding to 1406, the smart connection manager may determine classification of data to be transmitted. In an aspect of the present disclosure, data to be transmitted must be classified with respect to criticality. Transmission policy may be established for the smart connection manager to receive an indication of the type of data to be transmitted and to classify that data according to the indicated type. Examples of data to be transmitted are discussed in several embodiments herein.

Transmission policy for data classification by the smart connection manager may generally follow certain rules of classification in an example embodiment. Further, policy may follow some guidelines with respect to criticality classification in some embodiments. For example, data that involves ongoing communication or highly critical data for ongoing operations to proceed may be classified as high priority to avoid undue interruption of the users. Data involving burst data transfers or substantial queuing capability may have a lesser priority. Data that involves extremely high throughput requirements may be of lesser priority in some cases due to its effect on other data transmission. Periodically transmitted data, such as background data transfers, may be an example of lowest priority data. For example, IoT data involving monitoring of sensors and the like and that may periodically transmit data to a remotely-located IoT big data tracking software application system may often be of low priority.

In several aspects, data content, such as tracked IoT data content, may determine or impact classification levels of priority assigned to data to be transmitted at 1406. In an example, tracked IoT relating to mission critical key performance indicators for critical aspects of a system's operation, safety monitoring, or health monitoring key performance indicators for user biometrics, may be assigned a higher priority classification. In some example embodiments, data classification may even include certain types of data whereby a smart connection manager may elect not to transmit the data and instead store the data locally to avoid undue burden on the established wireless link when wireless conditions at a location are poor.

Table 1 below shows and example embodiment to priority classification for data to be transferred.

TABLE 1 High Medium Low Store Communication Service Types Priority Priority Priority Locally Voice X Video Conferencing X Web Browing X Text Message X Security Video X Video Stream X Audio Streaming Mission Critical Enterpise KPIs X Instrumentation for Offline X Diagnostics Operational Measurements X Instant Message X Mission Critical Health KPIs X Financial Transaction X Real Time Gaming Services X Multimedia Services (Picture X Text, Audio Messages) Driving Telemetry X

Flow may proceed to 1408 where the smart connection manager may determine if data to be transmitted is high-critical or of high priority. Examples shown above in Table 1 include voice data, video conferencing data, mission critical key performance indicators and the like. If the data to be transmitted is determined to be highly critical, then flow may proceed to 1410 where the data will be transmitted regardless of the wireless conditions if transmission is possible. In an example embodiment, the smart connection manager may seek to switch wireless links, for example when multiple wireless links are simultaneously established for a mobile information handling system. According to embodiments described, a plurality of wireless links may be available. The smart connection manager will seek to connect to the best option when wireless conditions are poor for the wireless link prepared for the predicted future path. The smart connection manager may then select from among the available options at the location along the predicted future path to transmit the highly critical data. In yet another example embodiment, a new wireless link may be established by the smart connection manager despite previous selection for a wireless link for a predicted future path. For highly critical data, the process of ranking and selecting an optimal wireless link for a location may be started anew to ensure transmission of such data in some embodiments. When transmission does occur, data will be transmitted according to regular queuing and transmission procedures by the wireless adapter. At this point, the flow may end.

If some of the data to be transmitted is not classified as highly critical, flow proceeds to 1412. At 1412, the smart connection manager will determine if the priority level is medium critical for the data to be transferred. If so, the smart connection manager will determine if the wireless link rating for one or more established optimal links meets a first level of minimum QoS at 1414. In other words, an assessment of the classification of wireless conditions for the established wireless link at a location will be made by the smart connection manager. In an example embodiment, if the wireless conditions for a current location along the predicted future path are good, then transmission may be scheduled currently or in the near future. Flow will proceed to 1416 to assess future locations along the predicted future path for wireless condition classification.

If future locations along the predicted future path meet or exceed the first level of minimum QoS or are classified as good or better wireless conditions, then flow may proceed to 1410 where the data is transmitted or scheduled to do so in the future according to normal queuing operation of the wireless adapter. If nearby future locations along the predicted future path are classified below good wireless conditions at 1416, this may trigger scheduled acceleration of the data transmission at 1418. At 1418, the smart connection manager may determine when the substantial drop in wireless conditions is to occur along the predicted future path and accelerate scheduling the medium critical data to be transmitted accordingly. Flow may then proceed to 1410 where the medium critical data is transmitted or scheduled to do so in the future according to normal queuing operation of the wireless adapter.

Returning to 1414, if a current location does not meet the 1^(st) level of minimum QoS or is not classified as having good wireless conditions, flow may proceed to 1420. At 1420, the smart connection manager determines not to transmit the medium critical data immediately. Accordingly, the smart connection manager may determine to schedule a delayed transmission of the medium critical data.

At 1422, the smart connection manager will determine the classification of the wireless conditions at a future location. If the future location does not correspond to a good wireless condition classification by meeting the first level of minimum QoS, flow proceeds to 1424. At 1424, another future location along the predicted future path is selected. Flow returns to 1422 where the smart connection manager will determine the classification of the wireless conditions for the next selected future location. This will repeat until a future location is found to meet the good wireless condition classification by meeting the minimum QoS level. The smart connection manager will determine that the wireless conditions for a wireless link at a selected future location exceeds a link rating level. It is understood that in an embodiment, the selection of future locations along the predicted future path may proceed based on nearness to the present location of the mobile information handling system. In another embodiment, additional future locations progressively further away may be analyzed at 1422. In yet other embodiments, a survey may be taken of all future locations relating to wireless condition classifications and one corresponding to meeting QoS levels for transmission of medium critical data may be selected. In the example embodiment described, a future location classified with good wireless conditions may be selected.

At 1426, the smart connection manager may schedule transmission of the medium critical data at the future location along the predicted future path. In an aspect, the smart connection manager may schedule a delay of the transmission until the time when the future location having good wireless conditions is reached. Flow will then proceed to 1428 where a delayed link indicator may be displayed to a user in some embodiments to indicate a scheduled delay for transmission of data. The delayed link indicator may be useful for some types of data such as a delay in transmission of a text message, web browsing data, an instant message or the like. Other data, such as IoT system monitoring data may not display a delayed link indicator since background data transmission may not be actively noticed by the user. At this point, the flow may end for medium critical data to be transmitted.

Returning to 1412, if there is data to be transmitted that is neither highly critical or medium critical, then flow may proceed to 1430. At 1430, the data to be transmitted is classified as low-critical in the presently described embodiment. It is understood that any number of data criticality classifications may be utilized by the smart connection manager in various embodiments. Further, an additional data criticality level is contemplated whereby a smart connection manager may determine to store data locally due to wireless conditions rather than schedule transmission. This data criticality classification is illustrated above in Table 1.

Continuing to FIG. 14B, the flow proceeds to 1432. At 1432, the smart connection manager will determine if the wireless link rating for one or more established optimal links meets a second level of minimum QoS. In other words, an assessment of the wireless conditions classification for the established wireless link will be made by the smart connection manager. In an example embodiment, if the wireless conditions for a current location along the predicted future path are great conditions, then transmission may be scheduled currently or in the near future for the low-critical data.

Similar to the previously described iteration, flow may proceed to 1434 to assess future locations along the predicted future path for wireless condition classifications. If future locations along the predicted future path meet or exceed the second level of minimum QoS or are classified as great wireless conditions, then flow may proceed to 1438. At 1438, the low-critical data is transmitted or scheduled to do so in the future according to normal queuing operation of the wireless adapter. If nearby future locations along the predicted future path are below the second level of minimum QoS or are classified below great wireless conditions at 1432, then this may trigger scheduled acceleration of the data transmission at 1436. The smart connection manager may determine when the substantial drop in wireless conditions is to occur along the predicted future path and accelerate scheduling the low critical data to be transmitted beforehand accordingly. Flow may then proceed to 1438 where the low-critical data is in fact transmitted or scheduled to do so in the future according to normal queuing operation of the wireless adapter. After transmission, the flow may end.

Returning to 1432 however, if a current location does not meet the second level of minimum QoS, then flow may proceed to 1440. Similar to the previously described iteration, the smart connection manager determines that the current location does not meet minimum QoS standards and determines to not immediately transmit low critical data. Accordingly, the smart connection manager may determine to schedule a delayed transmission of the low critical data.

At 1442, the smart connection manager will assess the classification of wireless conditions at a future location along the predicted future path if immediate transmission will not occur. If the future location does not correspond to a great wireless condition classification by meeting the second level of minimum QoS, then flow proceeds to 1444. The smart connection manager will select another future location along the predicted future path at 1444. Flow returns to 1442 where the smart connection manager will determine the classification of the wireless conditions for the next selected future location. This will repeat until a future location is found meeting the great wireless condition classification. In other words, at least the second level of minimum QoS is met. It is understood that the selection of future locations along the predicted future path may proceed based on nearness to the present location of the mobile information handling system in some aspects. In an embodiment, future locations may be analyzed at progressively further distance from the current location until the next closest future location meeting the second level of minimum QoS standards is met. In yet other embodiments, a survey may be taken of all future locations relating to wireless condition classifications and one location classified as having great conditions may be selected. In the example embodiment described, a future location with great wireless conditions may be selected due to reduce interference with wireless link bandwidth and efficient data transfer due to the excellent connectivity.

Proceeding to 1446, the smart connection manager may schedule transmission of the low-critical data. Transmission is scheduled to coincide with the future location along the predicted future path determined to meet the wireless location classification having great wireless conditions. Low-critical data may be rescheduled for transmission during great wireless conditions to increase efficiency of transmission. Transmission during less than great conditions may incur additional processing and energy resources. Further, bandwidth may be reduced during less than great wireless conditions, so rescheduling transmission of low critical data until great wireless conditions exist will no occupy bandwidth that may be used for higher priority data. Accordingly, the smart connection manager may schedule a delay of the transmission until the time when the future location has great wireless conditions because an acceptable minimum QoS level is reached.

Flow will then proceed to 1448 where a delayed link indicator may be displayed to a user in some embodiments to indicate a scheduled delay for transmission of data similar to described above. At this point, the flow may end for low critical data to be transmitted.

In another aspect when data transmission is delayed, the smart connection manager may transmit a placeholder data pointer to a recipient wireless adapter. Although the medium-critical and low-critical classified data may not be immediately transmitted according to embodiments above, the placeholder data pointer will indicate to the recipient wireless adapter where to find data that was to be transmitted. In the event of a failure to send at a later scheduled time, a software application at system having the recipient wireless adapter may use the placeholder data pointer to fetch the additional data. For example, a remotely located big data IoT analysis application may issue a request to receive IoT monitoring data delayed when conditions were below a minimum QoS level. It is understood that the placeholder data pointer will be of a relatively small data size when transmitted in lieu of medium or low-critical data.

A similar algorithm to the above algorithm for FIG. 14A and FIG. 14B may be performed to determine not to transmit data at all. Typically low critical classified data or data that would consume substantial resources to transmit may instead be elected to be not transmitted. That data may instead be stored locally. In some cases when the data is elected to be stored locally rather than scheduled for transmission, the smart connection manager may transmit the placeholder data pointer to an intended recipient wireless adapter. In such an embodiment, the software application operating at the intended recipient wireless adapter will have information on where to access the non-transmitted data if required.

FIG. 15A and FIG. 15B show an embodiment for operation of a context aware radio resource management system scheduling transmission of data according to the present disclosure. With the smart connection manager, a mobile device such as a smart vehicle gateway may schedule transmission of data according to levels of priority of the data and expected wireless conditions along a predicted future path to improve efficiency of bandwidth usage. As will be seen, data transmission scheduling may postpone transmission when conditions at a location are poor. In other aspects of the present disclosure, anticipated poor wireless conditions at future locations along the predicted future path may trigger scheduling of data transmission to be accelerated when a current location has great wireless conditions.

In another aspect of the present disclosure, the smart connection manager may determine radio link security levels for one or more available wireless links along a predicted future path. Required security level for data transmission may be factored into determinations of data transmission scheduling as well. In some circumstances, data to be transmitted may require highly secure wireless connectivity. Other data may only require lower levels of security for the wireless connection. Examples of security levels may be understood with respect to collection of monitored IoT data. Various types of IoT data content may determine necessary security levels. In one example embodiment, IoT biometric data for a user may require high levels of security due to privacy considerations. In another example embodiment, IoT data collected for normal vehicle operation such as tire pressure, gas mileage, or the like may require a lower level of security for wireless reporting back to an enterprise fleet vehicle tracking system.

The smart connection manager may adapt data and communication flow according to radiofrequency conditions and traffic usage levels as well as security level requirements to schedule data transmission during travel along a predicted future path. It is understood that data transmission may be less efficient and require more computing resources due to encryption and security measures for high security transmissions. Thus, a smart connection manager may elect to schedule data transmission on a wireless adapter based in part on security level requirements. In some embodiments as described, multiple optimal wireless links may be established for a mobile device. The multiple wireless links may transmit and receive data while utilizing varying levels of security measures. A smart connection manager may select between multiple optimal wireless link protocols established for the mobile devices such as a smart vehicle gateway during travel along a predicted future mobile path.

The method of FIG. 15A begins a 1502. At 1502, one or more optimal wireless links may be selected for use by a mobile information handling system during travel along a predicted future path. Using the context aware radio resource management system, a determination of optimization scoring for the available wireless links at a several locations along the predicted future path is made in accordance with disclosures herein. Determination of optimal wireless links for the predicted future path of a mobile information handling system may be made in accordance with embodiments described along with FIG. 13. A list of optimal wireless link options will be generated for the predicted future path. The list of optimal wireless service carriers and available wireless protocols will be used to select one or more wireless link protocols from among the optimal wireless links to establish communication to a WWAN or WLAN along the predicted future path.

Additionally at 1502, wireless conditions are classified at several locations along a predicted future path of a mobile information handling system. In an aspect of the present disclosure, the wireless condition classification for the future locations may be conducted for a plurality of selected optimal wireless links established for use along the predicted future path.

The context aware radio resource management system will classify predicted wireless performance at locations along the predicted future mobile path for each of the established wireless links. In the presently described example embodiment, two classifications may be used. One classification is for good wireless conditions and another for poor wireless conditions. An example of locations with two wireless connectivity ratings is shown in the embodiment of FIG. 10B as described above. It is understood that more than two wireless condition classifications may be used as shown in FIG. 10C.

Thresholds for link ratings may be used and applied for the established optimal wireless links at locations along the predicted future path. Application of a link rating threshold for future locations is compared to a link rating for the wireless links at each location. If the wireless link has a link rating that meets the threshold, a good wireless condition classification is made. If the wireless link has a link rating below the link rating threshold, then a poor wireless condition classification is applied to the location. In other embodiments, multiple link rating thresholds may be applied when more than two wireless condition classifications are to be used for locations along the predicted future path. Link ratings are derived for wireless links from crowd-sourced wireless reports for each of the future locations as described above. It is understood that factors such as link energy consumption and cost of data transfer or communication may influence link rating determinations in some embodiments. In accordance at least one embodiment of the present disclosure, weighting may be used in determination of link ratings to include rating for QoS parameters as well as energy consumed during data transfer as reported from crowd sourced data. In accordance with another embodiment of the present disclosure, data cost factors may be applied to weight link rating calculations. Wireless links are rated for QoS parameters as well based on cost per byte. In this way, one or both of factors such as link energy consumption and data transfer costs may be included in link ratings. When implemented with link ratings, energy consumption and cost factors may then also be reflected in the wireless condition classifications for future locations along the predicted future path.

Link rating thresholds may be selected to classify expected wireless conditions for an established wireless link as good or poor. The established wireless link classification for locations along the predicted future path may enable scheduling of data transmission as described in the present disclosure. Transmission of data under poor wireless conditions may require additional energy and limit transmission bandwidth available for data having a higher priority. Thus, data with a lower criticality classification may be beneficially scheduled to transmit at locations with better wireless conditions. This may free up bandwidth during poor wireless conditions and increase efficiency of transmission during travel along a predicted future path as described herein. The above thresholds for wireless link classification may serve as levels of minimum QoS corresponding to transmission scheduling determinations as described below. A level of minimum QoS as used in the present embodiment may also reflect the influence of link energy consumption or data cost as described above.

At 1504, the smart connection manager will determine what level of security is applied to a wireless links to be used along the predicted future path. The level of security will be determined from factors including exchange of credentials required, encryption, message verifications, and other known data security measures taken when transmitting data on a selected optimal established wireless link. In an example embodiment, three security levels may be applied including available strong security, moderate security, and low security. [INVENTOR QUESTION: More detail?]

Proceeding to 1506, the smart connection manager may receive data to be transmitted. In an embodiment, data to be transmitted by a mobile information handling system is queued and prepared for transmission by the wireless adapter. In a further embodiment, where the mobile information handling system is a mobile gateway device, data from IoT devices and information handling systems in a local network are transmitted to the mobile gateway device. Data is received at the mobile gateway device for pass-through transmission. Data transmission may be controlled by the smart connection manager on one or more established optimal wireless links according to disclosures herein.

At 1508, the smart connection manager may determine classification of data to be transmitted. The classification will relate to the criticality of the data based on priorities of importance for that data to be transmitted. Transmission policy may be established for the smart connection manager to receive an indication of the type of data to be transmitted. With the type of data indicated, the data is classified according to the indicated type. Examples of data to be transmitted are discussed in several embodiments herein.

Transmission policy for data classification by the smart connection manager may be determined according to certain rules of classification. The transmission policy may follow some guidelines with respect to the criticality classification to prioritize the data for transmission. In an example embodiment, data involving ongoing communication or highly critical data to system operations may be classified as high priority to avoid undue interruption of the users. Data involving burst data transfers or substantial queuing capability may have a lesser priority. Data that involves extremely high throughput requirements may be of reduced priority in some cases due to its effect on other data transmission. Periodically transmitted data may be an example of lowest priority data. For example, IoT data involving monitoring of sensors and the like and that may be periodically transmitted to a remotely-located IoT big data tracking software application system may have low priority.

In several aspects, data content may impact data criticality classification. For example, tracked IoT data content may impact classification levels of priority assigned. In an example aspect, tracked IoT relating to mission critical key performance indicators relating to critical aspects of a system operation may be assigned a highly critical classification. Similarly data for IoT safety monitoring or health monitoring of user biometrics may also be assigned a highly critical classification. IoT data reporting routine system operation data such as routine vehicle operational details or periodic atmospheric conditions such as temperature or the like may be assigned lower criticality classification.

In some example embodiments, data classification may even include one classification for certain types of data whereby a smart connection manager may elect not to transmit the data under poor wireless conditions. Instead, a smart connection manager may store the data locally to avoid undue burden on the established wireless link when wireless conditions at a location are poor. Several examples of data criticality classifications are found above in Table 1.

Proceeding to 1510, the smart connection manager may determine if data to be transmitted is highly critical and thus has a high priority for transmission. The examples shown above in Table 1 include voice data, video conferencing data, mission critical key performance indicators and the like. If the data to be transmitted is determined to be highly critical, then flow may proceed to 1510 where the level of security required for the data is determined. For example certain mission critical key performance indicators may be sensitive as well and require a minimum level of encryption and authentication. As such, a higher level of wireless link security will be required for transmission of such data. If the wireless link security classification corresponds to the required security level for the data to be transmitted, then flow proceeds to 1514 where the high priority data will be transmitted. Since the data is classified as highly critical, it will be transmitted regardless of the wireless conditions, if transmission is possible. At this point, the flow may end.

If at 1512 the wireless link security level is unavailable on a wireless link at the location, then flow may proceed to 1516. In accordance with security applied to wireless transmissions, the smart connection manager may activate key data encryption for the highly critical data. The key data encryption may be applied specific to the transmitting device and for the location along the predicted future path of the mobile information handling system. Key data encryption is understood and may be applied a number of ways. For example, symmetric key encryption or public key encryption may be used. Further levels of security may be applied to the transmission by the smart connection manager including message verification. Message verification may include use of message authentication codes or digital signature requirements with each wireless transmission. Upon application of sufficient security measure to the highly critical data to be transmitted, flow will proceed to 1514 for transmission according to regular queuing and transmission procedures for a wireless adapter. At this point, the flow may end with respect to the highly critical data.

If some of the data to be transmitted is not classified as highly critical, flow proceeds to 1518. In the present embodiment, two levels of data criticality classification are used. Thus at 1518, the data to be transmitted is classified as having low criticality or is described in the present embodiment as low-critical data. It is understood that multiple levels of criticality classification may be used in other embodiments such as those described above with respect to FIG. 14.

Proceeding to 1520, the smart connection manager will determine if the wireless link rating for one or more established optimal links meets a level of minimum QoS. An assessment of the classification of wireless conditions for the established wireless link at the current location will be made by the smart connection manager. In an example embodiment, if the wireless conditions for a current location along the predicted future path are good for one or more established wireless links, then transmission may be scheduled currently or in the near future.

Flow will proceed to 1522 to assess the security level of the one or more wireless links available at the current location along the predicted future path. The smart connection manager will determine if the available security measures for the wireless links at the location meet the required levels for the low critical data to be transmitted. Although the data is low-critical and has low priority for transmission, this does not necessarily mean that the low-critical data is not sensitive requiring high levels of security to be applied to transmission.

Strength of security level of a wireless link at a location depends on whether any of several security requirements are necessary to transmit on the wireless link. Security requirements include how much required encryption is needed and whether security verification is necessary for transmission of data along a wireless link. How stringent or how many security requirements are necessary are used to indicate whether security was classified as strong, moderate, or low in an example embodiment. [INVENTOR QUESTION: Is this correct?]

If security level is strong enough to match the requirements of the low-critical data to be transmitted and the minimum level of QoS is available at the current location, then flow proceeds to 1514 and the data is transmitted according to regular queuing and wireless adapter processing. Flow may end at this point for the low-critical data. If, however, the security level of at least one available wireless link does not meet the minimum level of required security for the low-critical data to be transmitted, then flow proceeds to 1524. As described, the smart connection manager may activate key data encryption for the low-critical data to a level of security as needed at 1524. Several security measures may be employed to reach a moderate or strong security across the available wireless link. For example, symmetric key encryption or public key encryption may be used as is understood. Further levels of security may be applied including message verification. It is understood that examples of message verification include use of message authentication codes or digital signature requirements with the wireless transmission. Upon application of security measures, at 1514 the low-critical data will be transmitted according to regular queuing and transmission procedures. At this point, the flow may end with respect to the low-critical data.

Returning to 1520, if no available wireless link at the current location meets the minimum QoS requirements to enable transmission of low-critical data according to the present disclosure, then flow proceeds to 1526. For example, the wireless link ratings at the current location may not meet a threshold link rating level to be classified as having good wireless conditions. In such a case, the smart connection manager determines that immediate transmission of the data will not occur.

It is understood that expected QoS levels may not be the only criteria determinative of the wireless conditions. In some embodiments as described herein, link ratings may be affected by energy link reports relating to levels of power consumed in transmitting on a particular wireless link. Battery power state levels for a mobile information handling system will further impact the how significant energy consumption by a wireless link will be in the wireless condition classification in accordance with disclosures herein. For example, a low battery power state will increase the need for highly-efficient power consumption during transmission of data. Link ratings of wireless links to be classified as having good or great wireless conditions will need to have lower levels of power consumption during transmission and reception as described above. Link ratings will be determined by radio QoS parameters as well as other aspect of link energy reports described above. When a mobile information handling system has an AC connection or has substantial power available, link rating determination will rely less on energy link reporting to impact classification of wireless conditions at a location. For example, a smart vehicle gateway may have substantial power resources available from a vehicle and may rely less on energy link reports to determine link ratings.

In other example embodiments, wireless link cost considerations may affect link ratings and therefore criterion for classifying a wireless link at locations along a predicted future mobile path. In accord with the present disclosure, an IT manager or user may elect to avoid high-cost wireless connections and may weight link ratings according to cost of wireless access. In an example embodiment, a location may require a roaming charge for access to a wireless protocol on a service provider. This may incur substantial additional cost. Thus, a roaming charge may trigger an algorithmic adjustment to the link rating for a wireless link at a location along the predicted future path. In this way, the cost of data transmission may additionally affect classification of wireless conditions for the present disclosure. At 1526, the smart connection manager may determine to delay transmission of the low-critical data.

Continuing to FIG. 15B, the flow proceeds to 1528. The smart connect manager will proceed to 1528 to assess the classification of wireless conditions at one or more future locations along the predicted future path. If a future location does not correspond to a good wireless condition classification or meet the level of minimum QoS, flow proceeds to 1530. At 1530, another future location along the predicted future path is selected for assessment. Flow returns to 1528 where the smart connection manager will determine the classification of the wireless conditions for the next selected future location. This process will repeat until a future location is found that meets the good wireless condition classification by meeting the minimum QoS level. It is understood that in an embodiment, the selection of future locations along the predicted future path for assessment at 1528 may proceed based on nearness to the present location. In another example embodiment, assessment of future locations at 1528 may involve a survey of all wireless condition classifications at future locations. FIGS. 10B and 10C show optional detail of how the smart connection manager may survey the future locations along a predicted future path. The smart connection manager will select a future location having a good wireless condition in the present embodiment that corresponds to conditions favorable for transmission of low-critical data at 1528. In the example embodiment described, a future location with good wireless conditions may be selected as shown in FIG. 10 B, but in other embodiments several classifications may be used as shown in FIG. 10C.

Flow will proceed to 1532 to assess security level requirements for wireless links available and meeting the minimum QoS levels to transmit low-critical data. Determination of whether one or more security requirements are necessary to transmit on a wireless link indicates whether security was classified as strong, moderate, or low in an example embodiment. Security requirements include how much required encryption is needed and whether security verification is necessary for transmission along a wireless link. [INVENTOR QUESTION: Is this correct?] If a wireless link at a future location does not meet the security level requirement, flow may return to 1530 to select a new future location along the predicted future path. In this embodiment, the smart connection manager will proceed to assess wireless conditions for the future location as well as the security level for transmission available at the next future location in accordance with 1528 and 1532 to determine a future location sufficient for transmission of low-critical data.

In another example embodiment (not shown), the smart connection manager may instead implement key data encryption for the mobile information handling system at the future location that meets a minimum QoS level if the security level requirement is not met. This may be done in accordance with the description above for 1524. Once a future location has been selected along the predicted future path meeting the wireless performance classification and security requirements, the smart connection manager may proceed to 1534.

At 1534, the smart connection manager may schedule transmission of the low-critical data. In an aspect, the smart connection manager may schedule a delay of the transmission until the time when the future location having good wireless conditions and an acceptable minimum security level is reached. At this point the flow may end.

It is understood that the above steps in the several flow charts described herein for scheduling, determination of a predicted future path, or determination of optimal wireless links may occur in any order and some steps may be omitted or additional steps may be added in various embodiments. Additional steps not described but understood by those of skill may also be performed. Also, additional steps described in various embodiments herein may also be combined and performed in any order to accomplish the data transmission or data scheduling determinations of the present disclosure. It is anticipated that aspects from various embodiment descriptions of data transmission scheduling may be implemented in any order and with combinations of factors described herein. Data transmission or data scheduling determinations may be based on factors including wireless QoS conditions predicted for current and future locations, energy link consumption, security, or cost of data transmission on one or more available wireless links. Further, any of several criteria may be used for criticality classification of data to be transmitted. Further, the data transmission or data scheduling determinations made according to the present disclosure are made for mobile information handling systems including devices operation as mobile gateway devices such as smart vehicle gateways or smart personal connect gateways according to various embodiments described herein.

FIG. 16 shows an information handling system 1600 capable of administering each of the specific embodiments of the present disclosure. The information handling system 1600 can represent the mobile information handling systems 110, 120, and 130, the smart vehicle gateway 135, or servers or systems located anywhere within network 100 of FIG. 1, including the remote data center 186 operating virtual machine applications, and the context aware radiofrequency resource management system 190 described herein. The information handling system 1600 may include a processor 1602 such as a central processing unit (CPU), a graphics processing unit (GPU), or both. Moreover, the information handling system 1600 can include a main memory 1604 and a static memory 1607 that can communicate with each other via a bus 1608. As shown, the information handling system 1600 may further include a video display unit 1610, such as a liquid crystal display (LCD), an organic light emitting diode (OLED), a flat panel display, a solid state display, or a cathode ray tube (CRT). Additionally, the information handling system 1600 may include an input device 1612, such as a keyboard, and a cursor control device, such as a mouse. The information handling system may include a power source such as battery 1614 or an A/C power source. The information handling system 1600 can also include a disk drive unit 1616, and a signal generation device 1618, such as a speaker or remote control. The information handling system 1600 can include a network interface device such as a wireless adapter 1620. The information handling system 1600 can represent a server device whose resources can be shared by multiple client devices, or it can represent an individual client device, such as a desktop personal computer, a laptop computer, a tablet computer, or a mobile phone.

The information handling system 1600 can include a set of instructions 1624 that can be executed to cause the computer system to perform any one or more of the methods or computer based functions disclosed herein. For example, instructions 1624 may execute the context aware radio resource management system including a path prediction system and other aspects as disclosed herein. In another aspect, instructions 1624 may execute the smart connection manager system disclosed herein to perform data transmission scheduling as described in several the embodiments. In a further example, processor 1602 may conduct processing of wireless service usage by the information handling system 1600 according to the systems and methods disclosed herein. The computer system 1600 may operate as a standalone device or may be connected such as using a network, to other computer systems or peripheral devices.

In a networked deployment, the information handling system 1600 may operate in the capacity of a server or as a client user computer in a server-client user network environment, or as a peer computer system in a peer-to-peer (or distributed) network environment. The information handling system 1600 can also be implemented as or incorporated into various devices, such as a personal computer (PC), a tablet PC, a set-top box (STB), a PDA, a mobile information handling system, a palmtop computer, a laptop computer, a desktop computer, a communications device, a wireless smartphone, a land-line telephone, a control system, a camera, a scanner, a facsimile machine, a printer, a pager, a personal trusted device, a web appliance, a network router, switch or bridge, or any other machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. In a particular embodiment, the computer system 1600 can be implemented using electronic devices that provide voice, video or data communication. Further, while a single information handling system 1600 is illustrated, the term “system” shall also be taken to include any collection of systems or sub-systems that individually or jointly execute a set, or multiple sets, of instructions to perform one or more computer functions.

The disk drive unit 1616 may include a computer-readable medium 1622 in which one or more sets of instructions 1624 such as software can be embedded. The disk drive unit 1616 also contains space for data storage. Further, the instructions 1624 may embody one or more of the methods or logic as described herein. For example, instructions relating to the context aware radio resource management software algorithms may be stored here. Additionally, parameters and profiles relating to context aware radio resource management system may be stored here. Parameters may include communication and efficiency rules or data relating to device-specific capabilities. Profiles stored here may include end-user profile data measured by the processor 1602 during wireless service usage processing. Profiles may additionally include crowd source spatial-temporal radio frequency profiles for wireless links or energy link consumption data. In a particular embodiment, the instructions, parameters, and profiles 1624 may reside completely, or at least partially, within the main memory 1604, the static memory 1607, and/or within the processor 1602 during execution by the information handling system 1600. The main memory 1604 and the processor 1602 also may include computer-readable media. Battery 1614 may include a smart battery system that tracks and provides power state data 1626. This power state data may be stored with the instructions, parameters, and profiles 1624 to be used with the systems and methods disclosed herein.

The network interface device shown as wireless adapter 1620 can provide connectivity to a network 1628, e.g., a wide area network (WAN), a local area network (LAN), wireless local area network (WLAN), a wireless personal area network (WPAN), a wireless wide area network (WWAN), or other network. Connectivity may be via wired or wireless connection. One or more wireless adapters 1620 may be implemented including wireless adapters geared to upstream communications or a wireless adapter for local communications via Bluetooth, WiFi, Zigbee or other communication protocols. Wireless adapter 1620 may include one or more radio frequency subsystems 1630 with transmitter/receiver circuitry, wireless controller circuitry, amplifiers and other circuitry for wireless communications. Each radiofrequency subsystem 1630 may communicate with one or more wireless technology protocols. The radiofrequency subsystem 1630 may contain individual subscriber identity module (SIM) profiles for each technology service provider and their available protocols. Alternatively, it may have a software based SIM profile that is reconfigurable, referred to as an electronic SIM or (eSIM). In some embodiments as described herein, a plurality of eSIMs may be used with one or more wireless adapters 1620. The wireless adapter 1620 may also include antenna system 1632 which may be tunable antenna systems for use with the system and methods disclosed herein.

In an alternative embodiment, dedicated hardware implementations such as application specific integrated circuits, programmable logic arrays and other hardware devices can be constructed to implement one or more of the methods described herein. Applications that may include the apparatus and systems of various embodiments can broadly include a variety of electronic and computer systems. One or more embodiments described herein may implement functions using two or more specific interconnected hardware modules or devices with related control and data signals that can be communicated between and through the modules, or as portions of an application-specific integrated circuit. Accordingly, the present system encompasses software, firmware, and hardware implementations.

In accordance with various embodiments of the present disclosure, the methods described herein may be implemented by software programs executable by a computer system. Further, in an exemplary, non-limited embodiment, implementations can include distributed processing, component/object distributed processing, and parallel processing. Alternatively, virtual computer system processing can be constructed to implement one or more of the methods or functionality as described herein.

The present disclosure contemplates a computer-readable medium that includes instructions, parameters, and profiles 1624 or receives and executes instructions, parameters, and profiles 1624 responsive to a propagated signal; so that a device connected to a network 1628 can communicate voice, video or data over the network 1628. Further, the instructions 1624 may be transmitted or received over the network 1628 via the network interface device or wireless adapter 1620.

Information handling system 1600 includes one or more application programs 1624, and Basic Input/Output System and firmware (BIOS/FW) code 1624. BIOS/FW code 1624 functions to initialize information handling system 1600 on power up, to launch an operating system, and to manage input and output interactions between the operating system and the other elements of information handling system 1600. In a particular embodiment, BIOS/FW code 1624 reside in memory 1604, and include machine-executable code that is executed by processor 1602 to perform various functions of information handling system 1600. In another embodiment (not illustrated), application programs and BIOS/FW code reside in another storage medium of information handling system 1600. For example, application programs and BIOS/FW code can reside in drive 1616, in a ROM (not illustrated) associated with information handling system 1600, in an option-ROM (not illustrated) associated with various devices of information handling system 1600, in storage system 1607, in a storage system (not illustrated) associated with network channel 1620, in another storage medium of information handling system 1600, or a combination thereof. Application programs 1624 and BIOS/FW code 1624 can each be implemented as single programs, or as separate programs carrying out the various features as described herein.

In several of the embodiments in the figures herein it is understood that application programs 1624 and BIOS/FW code 1624 may be used as sets of executable instructions to accomplish the computer implemented methods described in those figures. Each of the figures above is an exemplary embodiment and no order is required to perform the computer implemented method steps recited therein. Furthermore, while the embodiments of the figures above recite several method steps, some or all method steps may be omitted or other different method steps may be added. Additionally, it is understood that combinations and variations on the method steps recited in description of the embodiments for the figures above may be combined in various ways as well.

While the computer-readable medium is shown to be a single medium, the term “computer-readable medium” includes a single medium or multiple media, such as a centralized or distributed database, and/or associated caches and servers that store one or more sets of instructions. The term “computer-readable medium” shall also include any medium that is capable of storing, encoding, or carrying a set of instructions for execution by a processor or that cause a computer system to perform any one or more of the methods or operations disclosed herein.

In a particular non-limiting, exemplary embodiment, the computer-readable medium can include a solid-state memory such as a memory card or other package that houses one or more non-volatile read-only memories. Further, the computer-readable medium can be a random access memory or other volatile re-writable memory. Additionally, the computer-readable medium can include a magneto-optical or optical medium, such as a disk or tapes or other storage device to store information received via carrier wave signals such as a signal communicated over a transmission medium. Furthermore, a computer readable medium can store information received from distributed network resources such as from a cloud-based environment. A digital file attachment to an e-mail or other self-contained information archive or set of archives may be considered a distribution medium that is equivalent to a tangible storage medium. Accordingly, the disclosure is considered to include any one or more of a computer-readable medium or a distribution medium and other equivalents and successor media, in which data or instructions may be stored.

In the embodiments described herein, an information handling system includes any instrumentality or aggregate of instrumentalities operable to compute, classify, process, transmit, receive, retrieve, originate, switch, store, display, manifest, detect, record, reproduce, handle, or use any form of information, intelligence, or data for business, scientific, control, entertainment, or other purposes. For example, an information handling system can be a personal computer, a consumer electronic device, a network server or storage device, a switch router, wireless router, or other network communication device, a network connected device (cellular telephone, tablet device, etc.), or any other suitable device, and can vary in size, shape, performance, price, and functionality. The information handling system can include memory (volatile (e.g. random-access memory, etc.), nonvolatile (read-only memory, flash memory etc.) or any combination thereof), one or more processing resources, such as a central processing unit (CPU), a graphics processing unit (GPU), hardware or software control logic, or any combination thereof. Additional components of the information handling system can include one or more storage devices, one or more communications ports for communicating with external devices, as well as, various input and output (I/O) devices, such as a keyboard, a mouse, a video/graphic display, or any combination thereof. The information handling system can also include one or more buses operable to transmit communications between the various hardware components. Portions of an information handling system may themselves be considered information handling systems.

When referred to as a “device,” a “module,” or the like, the embodiments described herein can be configured as hardware. For example, a portion of an information handling system device may be hardware such as, for example, an integrated circuit (such as an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a structured ASIC, or a device embedded on a larger chip), a card (such as a Peripheral Component Interface (PCI) card, a PCI-express card, a Personal Computer Memory Card International Association (PCMCIA) card, or other such expansion card), or a system (such as a motherboard, a system-on-a-chip (SoC), or a stand-alone device). The device or module can include software, including firmware embedded at a device, such as Intel Core®, ARM® RISC, or other processor, or other such device, or software capable of operating a relevant environment of the information handling system. The device or module can also include a combination of the foregoing examples of hardware or software. Note that an information handling system can include an integrated circuit or a board-level product having portions thereof that can also be any combination of hardware and software.

Devices, modules, resources, or programs that are in communication with one another need not be in continuous communication with each other, unless expressly specified otherwise. In addition, devices, modules, resources, or programs that are in communication with one another can communicate directly or indirectly through one or more intermediaries.

Although only a few exemplary embodiments have been described in detail herein, those skilled in the art will readily appreciate that many modifications are possible in the exemplary embodiments without materially departing from the novel teachings and advantages of the embodiments of the present disclosure. Accordingly, all such modifications are intended to be included within the scope of the embodiments of the present disclosure as defined in the following claims. In the claims, means-plus-function clauses are intended to cover the structures described herein as performing the recited function and not only structural equivalents, but also equivalent structures. 

What is claimed is:
 1. An information handling system comprising: a storage device to store a spatial-temporal radio frequency profile indicating signal quality for wireless links available at a plurality of locations; an application processor to select a predicted future path for a mobile information handling system during a future time interval; the application processor to predict radio quality of a plurality of wireless links over the predicted future path selected for the mobile information handling system based on measured radio quality of service (QoS) parameters for the plurality of locations; the application processor selecting an optimal wireless link for the future time interval; the application processor to classify data to be transmitted on a wireless link based on data criticality; and a network interface to transmit a command to schedule transmission of data classified as non-critical until a future location is reached on the predicted future path with improved predicted radio quality if radio quality at a current location is poor.
 2. The system of claim 1, further comprising: the application processor to access a radio selection policy for the mobile information handling system wherein the radio selection policy depends on cost of wireless link usage, link quality of service, or link power consumption.
 3. The system of claim 1, wherein the measured radio QoS parameters for the plurality of locations are aggregated via crowd-sourced mobile wireless traffic reports.
 4. The system of claim 1, further comprising: the network interface to transmit a command to schedule acceleration of transmission of data classified as non-critical when a future location on the predicted future path has predicted radio quality that is poor.
 5. The system of claim 4, further comprising: a network interface device for transmitting an optimal wireless link selection to the mobile information handling system.
 6. The system of claim 1, further comprising: the application processor receiving data that the mobile information handling system device battery state is below a threshold level; and the application processor transmitting a command schedule transmission of data for a location on the predicted future path with best predicted radio quality when the data to be transmitted is classified as non-critical.
 7. The system of claim 1, further comprising: the application processor determining three or more wireless link rating classifications levels for locations over the predicted future path; and the application processor classifying data to be transmitted on a wireless link at three or more corresponding criticality levels.
 8. The system of claim 1, further comprising: the application processor receiving a minimum level of security requirements provided for the selected optimal wireless link; and the application processor determining if the minimum level of security requirements are sufficient for the classified data to be transmitted.
 9. A computer implemented method comprising: storing a spatial-temporal radio frequency profile indicating signal quality for wireless links available at a plurality of locations; selecting, via an application processor, a predicted future path for a mobile information handling system during a future time interval; and predicting radio quality of a plurality of wireless links over the selected future path for the mobile information handling system based on measured radio quality of service (QoS) criteria for the plurality of locations and selecting a first optimal wireless link for the future time interval; classifying data to be transmitted on the selected optimal wireless link based on data criticality; and scheduling acceleration of transmission of data classified as non-critical when a future location on the predicted future path has predicted radio quality that is poor relative to the current location.
 10. The method of claim 9, further comprising: scheduling delayed transmission of data classified as non-critical if radio quality at a current location is poor until a future location is reached on the predicted future path with improved predicted radio quality.
 11. The method of claim 9, further comprising: determining that data classified as non-critical data is to be stored locally and permanently delayed; and transmitting a placeholder data pointer to a recipient wireless adapter when transmission of data is scheduled to be permanently delayed.
 12. The method of claim 9, further comprising: selecting a second optimal wireless link for the future time interval.
 13. The system of claim 9, further comprising: switching data transmission between the first selected and second selected optimal wireless links depending on wireless link rating classifications levels for locations over the predicted future path; scheduling transmission on either the first selected or second selected optimal wireless links of data classified as non-critical if radio quality at the current location is poor.
 14. An information handling system comprising: a wireless adapter to communicate via a wireless link; a storage device to store a spatial-temporal radio frequency profile indicating signal quality for wireless links available at a plurality of locations; an application processor to select a predicted future information handling system path during a future time interval and to predict radio quality of a plurality of wireless links over the predicted future path based on measured radio quality of service (QoS) parameters for a plurality of locations in the predicted future path; the application processor selecting an optimal wireless link for the future time interval; the application processor to classify data to be transmitted on the selected optimal wireless link based on data criticality; and the application processor to schedule transmission of data classified as non-critical until a future location is reached on the predicted future path with improved predicted radio quality if radio quality at a current location is poor.
 15. The system of claim 14, further comprising: the wireless adapter to schedule acceleration of transmission of data classified as non-critical when a future location on the predicted future path has predicted radio quality that is poor relative to the current location.
 16. The system of claim 14, further comprising: the application processor selecting the optimal wireless link for the future time interval by matching QoS requirements of wireless devices in a local wireless network to transmit data through a mobile wireless gateway, wherein the information handling system is the mobile wireless gateway.
 17. The system of claim 14, further comprising: the application processor receiving data that the mobile information handling system device battery state is below a threshold level; and the application processor delaying transmission of data when radio quality at a current location is poor.
 18. The system of claim 14, further comprising: the wireless adapter transmitting a placeholder data pointer to a recipient wireless adapter when transmission of data is scheduled to be delayed.
 19. The system of claim 14, further comprising: the application processor receiving a minimum level of security requirements provided for the selected optimal wireless link; and the application processor determining if the minimum level of security requirements are sufficient for the classified data to be transmitted.
 20. The system of claim 14, further comprising: the application processor displaying a delayed link indicator to the user indicating a delayed data transmission. 